External Variables, Beliefs, Attitudes and Information Technology Usage Behavior Geoffrey S. Hubona Virginia Commonwealth University
[email protected]
Sarah Geitz Purdue University
[email protected] 2: Theory and background
Abstract The Technology Acceptance Model (TAM) predicts the user acceptance of end-user applications by specifying causal relationships among belief and attitudinal constructs that subsequently influence usage behavior. Although the perceived usefulness and perceived ease of use constructs have received a great deal of attention in MIS literature, very few follow-up studies have used the original TAM constructs. Moreover, the various studies investigating TAM use different measurement items, or factors, to assess the belief constructs. In this TAM study, the impact of external variables affecting usage behavior is examined. The results suggest that the impact of the external variables on usage behavior is not fully mediated by the belief constructs.
1: Introduction The Technology Acceptance Model (TAM) specifies causal relationships among select belief and attitudinal constructs to predict the user acceptance of end-user applications. Studies utilizing the perceived usefulness and perceived ease of use belief constructs have been widely published in recent MIS literature [1] [6] [7] [8] [10] [17] [18] [19] [20] [26] [27] [28] [30] However, relatively few studies have validated TAM using all original belief and attitudinal constructs. TAM asserts that the principal influence of the beliefs is on attitudes that subsequently impact usage behavior. The attitudinal construct, central to the theoretical basis for TAM, is widely omitted in previous studies. Further, the specific measurement items, or factors, that comprise the perceived usefulness and perceived ease of use constructs vary widely from study to study. In addition, the role of external variables in impacting usage behavior within TAM has not been well explored. Davis (1993) specifically urged (p.483): “future research [to] consider the role of additional [external] variables within TAM” The specific objectives of this study are to: (1) validate TAM using the original belief and attitudinal constructs; and (2) examine the direct and indirect influences of select external variables on usage behavior.
Fishbein and Ajzen’s [15] [16] Theory of Reasoned Action (TRA) and Davis’ [6] [7] [8] Technology Acceptance Model (TAM) provide theoretical contexts for measuring beliefs and attitudes to predict future behaviors. TAM adapts TRA specifically for modeling user acceptance of information systems. TAM asserts that two particular beliefs, perceived usefulness and perceived ease of use, are centrally important in predicting computer systems users’ acceptance behaviors. Figure 1 illustrates the TAM model [8]. Davis’ representation of TAM has evolved over time, notably with the exclusion of the intention to use construct when actual or selfreported usage measures are available. However, an attitudinal construct has always been included in Davis’ representations of TAM. TAM asserts that the influence of external variables upon user behavior is mediated through user beliefs and attitudes. Beliefs connote a degree of instrumentality tied to an action whereas attitudes are purely affective. Beliefs relate to an individual’s subjective assessment that performing some behavior will result in a specific consequence, whereas attitudes relate to an individual’s positive or negative affective feelings about performing the behavior. Perceived usefulness and perceived ease of use are both belief constructs. Davis et al. [9] defined perceived usefulness as (p. 985) “the prospective user’s subjective probability that using a specific application system will increase his or her job performance within an organizational context.” Perceived ease of use is (p. 985) “the degree to which the prospective user expects the target system to be free of effort.” Davis [7] originally developed the TAM constructs and validated the model in: (1) a field study assessing the self-reported usage of PROFS electronic mail and XEDIT file editor applications; and (2) a lab study of the intended usage of Chart-Master and Pendraw graphic systems. Adams, Nelson & Todd [1] replicated Davis’ original work in two field studies on the usage of: (1) electronic and voice mail; and (2) the WordPerfect, Lotus 1-2-3, and Harvard Graphics applications. In their first study of electronic and voice mail usage, Adams et al. applied Davis’ [7] original six item usefulness scale.
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However, they eliminated two of Davis’ original items from the ease of use scale. Furthermore, no measure of attitude was assessed. Instead, their model looked at the impact of usefulness and ease of use on two separate measures of usage as mediated by an additional latent usage construct. The usage measures consisted of users’
self-reports of (p. 229): “the number of messages they had sent and received on the previous working day, as well as the number they sent and received on a typical day.”
Perceived Usefulness
External Variables
Attitude Toward Using
Actual System Use
Affective Response
Behavioral Response
Perceived Ease of Use External Stimulus
Cognitive Response
Figure 1: Technology acceptance model. In their second study of WordPerfect, Lotus and Harvard Graphics usage, Adams et al. [1] again made no changes to Davis’ usefulness scale, but ease of use was modified. One ease of use item that was deleted from their first study (“It was easy to become skillful using _____”) was added, and they introduced an additional, new ease of use item (“It is easy to remember how to perform tasks using _____”). As in their first study, no measure of attitude was assessed. Instead, they looked at the impact of usefulness and ease of use on two measures of selfreported usage as mediated by an additional latent usage construct. Respondents recorded (p. 237) “their usage on a six point scale ranging from not at all through daily” and respondents reported “how many hours they used each package in the last week.” Other studies have reexamined TAM. Hendrickson, Massey & Cronan [18] looked at the test-retest reliability of the perceived usefulness and perceived ease of use scales with undergraduate students using the Lotus 1-2-3 spreadsheet and Paradox database applications. Subramanian [29] examined the impact of usefulness and ease of use in predicting future usage of voice mail and customer dial up systems. Each of these studies and others [10] [26] have largely reaffirmed the reliability and validity of the psychometric properties of the usefulness and ease of use constructs. However, the specific measurement factors used to assess these two constructs
have been inconsistent across these studies. Segars and Grover [28] noted that (p.525): “determining the structure of psychological constructs such as ‘ease of use’ and ‘usefulness’ is a complex activity . . of critical importance in accurately explaining levels of usage . .” Davis’ (1989) original instrument (see Appendix) contained six items for each of the usefulness and ease of use constructs and five items for the attitude toward using construct. Poor theory development [13] [25] and the inadequate or inconsistent measurement of constructs related to user perceptions of information technologies have been extensively reported in the literature. Many authors [21] [23] [4] have noted the problems of using inconsistent instruments. As noted by Moore and Benbasat [27], IS research requires a cumulative tradition that must be based on a shared set of definitions, topics and concepts. Many IS studies have focused on instrument development for IS research [3] [22] [14] [12] and a large number have specifically investigated the perceived usefulness and perceived ease of use constructs [1] [6] [7] [8] [9] [11] [18] [19] [20] [26] [27] [28] [29]. These two measures have sound theoretical foundations and are widely accepted as valid, predictive measures of future information system usage levels. The empirical research model is presented as Figure 2. Structural equation modeling and path analysis is utilized to estimate the model linkages. There are six variables in
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the empirical model. Three variables are latent constructs and three are measured variables. Latent variables include usefulness, ease of use and attitude constructs. The usefulness and ease of use constructs are measured with Davis’ [7] original six-item, seven-point semantic differential scales (see items 1 through 12 in the Appendix). Attitude toward using is measured with a fiveitem, seven-point semantic differential rating scale (see items 13 through 17 in the Appendix) as suggested by Ajzen and Fishbein [2] and Davis [8]. Measured (or
manifest) variables include external variables, usage frequency and usage amount. External variables include organizational job category (staff support, programmer, analyst, and manager), system experience, and computer experience. Usage frequency is measured as how often, on a weekly basis, the respondent reports using the application. Usage amount is measured as the number of hours per week that the respondent reports using the application.
Usage Frequency
Perceived Usefulness Attitude Toward Using
External Variables - Job Category - System Experience - Computer Experience
Perceived Ease of Use
Usage Amount
Figure 2: Empirical research model.
3: Research method 3.1: Subjects and procedure Subjects were 125 staff, professional and managerial employees of a large federal government agency in the mid-Atlantic states. A questionnaire was circulated that solicited their beliefs and attitudes about two different Microsoft windows end-user applications, cc:mail electronic mail and WordPerfect word processing software. Respondents were screened to ensure that they had used the target software applications. Of the 125 subjects, 122 had used the electronic mail package and 118 had used the word processor, for a total of 240 usable responses. 3.2: Construct measurement and validation The usefulness, ease of use and attitude constructs were measured using semantic differential scales. Usefulness and ease of use were measured with Davis’ [7] original six item scales, and attitude was measured using a five item scale [2] [8]. In the instrument (see Appendix), items 1 through 6 relate to perceived
usefulness, items 7 through 12 relate to perceived ease of use and items 13 through 17 relate to attitude toward using. The measured constructs include job category; years of computer experience; system experience; usage frequency and usage amount. To record job category, subjects circled one of four categories (staff, programmer, analyst, manager) in response to the question: “Which of the following best describes your employment category?” Years of computer experience was measured as the subjects’ self-report of their “years of experience using a computer for any purpose.” System experience was measured as subjects’ self-report of the “length of elapsed time since they first used the application.” Usage frequency and usage amount were the same as Davis’ [8] measures. For usage frequency, subjects circled one of six answers in response to the question “On the average, I use cc:mail: a. Don't use at all; b. Use less than once a week; c. Use about once each week; d. Use several times each week; e. Use about once each day; and f. Use several times each day.” For usage amount, subjects responded to the following question: “Please specify (estimate) how many hours each week you normally spend using cc:mail (WordPerfect): _____ hours.”
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Table 1: Factor analysis results. Item Number Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17
Perceived Usefulness 0.82 0.99 0.89 0.95 0.78 0.61 0.00 0.00 0.05 0.00 0.03 0.03 0.11 0.13 0.04 0.14 0.06
Perceived Attitude Ease of Use Towards Using 0.02 0.11 0.03 - 0.09 0.02 0.08 - 0.06 0.04 0.07 0.13 0.10 0.30 0.98 - 0.10 0.92 0.01 0.85 0.10 0.76 0.20 0.91 - 0.03 0.83 0.13 0.14 0.75 0.04 0.83 0.08 0.88 - 0.04 0.85 0.06 0.87
The survey questionnaire was designed to measure three distinct latent constructs: (1) perceived usefulness; (2) perceived ease of use; and (3) attitude toward using. Factor analysis was used to validate the measurement of these latent constructs, conducted on a sample size of N = 240. In all cases, factors were extracted using covariance matrices and the method of principal components. Oblique rotations were used to help interpret initial factor patterns. The factor loadings (see Table 1) provide evidence for the factorial validity of the three scales. In addition, Cronbach’s alpha is 0.97 for usefulness, 0.96 for ease of use, and 0.97 for attitude towards using, reflecting high levels of construct reliability. The raw data for computer experience and job category was linearly transformed to fit a measurement scale in the same range as the belief and attitudinal measures. The raw data for system experience and usage amount each exhibited right-skewed distributions that were rescaled by computing natural logarithms to create more symmetric distributions. Linear transformations were then performed on the rescaled distributions to fit a range similar to the other measured constructs.
4: Results The data was analyzed with structural equation modeling (e.g., PROC CALIS in SAS) using covariance analysis to estimate path coefficients. The purpose of the analysis was to assess the structural and measurement characteristics of two models: (1) the empirical research model which indirectly links the influence of the external
variables to the usage measures (see Figure 2); and (2) a revised model with additional direct links introduced between each of the three external variables and attitude, usage frequency and usage amount. Parameter values were estimated using the technique of generalized least squares (GLS). The GLS method of parameter estimation does not assume normality for variable distributions. Table 2: Fit measures for the a priori structural models. Recommended Values Chi-Square p > 0.05 Chi-Square/DF < 3.0 Goodness of Fit > 0.90 Adjusted GFI > 0.80 Bollen's Index > 0.90 RMSR < 0.5
Fit Measures p < 0.01 2.06 0.84 0.80 0.99 0.22
Table 3: TAM measurement model path coefficients. Path Coefficient Rho (Computer experience to usefulness) 0.08* Rho (Job category to usefulness) 0.13** Rho (Job category to ease of use) 0.16** Rho (System experience to ease of use) 0.19** Rho (Ease of use to usefulness) 0.67*** Rho (Usefulness to attitude) 0.66*** Rho (Ease of use to attitude) 0.26*** Rho (Attitude to usage frequency) 0.53*** Rho (Attitude to usage amount) 0.45*** * Significant at 0.10 ** Significant at 0.05 *** Significant at 0.001 Table 2 provides fit measures for the structural equations that test the path influences in the empirical research model. Although the chi-square analysis in Table 2 indicates a lack of fit, the value of chi-square divided by degrees of freedom is approximately 2.1, which is less than cutoff value of 5.0 used by Adams et al. [1] and recommended by Wheaton, Muthen, Alwin & Summers [31]. This adjusted chi-square value is also less that the cutoff value of 3.0 used by Segars & Grover [28]. As noted by Segars & Grover [28], the chi-square statistic is sensitive to large sample sizes with a large number of indicators, such that trivial discrepancies between a model and data can result in significant chisquare values. Therefore, other measures of model fit such as adjusted chi-square, goodness of fit indices, and root mean square residual should be considered [5] [24] [28]. The goodness-of-fit and adjusted-goodness-of-fit indices are close to recommended thresholds. In
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summary, the fit statistics in Table 2 reflect an acceptable structural model fit. Measurement model results are presented in table form (see Table 3) and graphically (see Figure 3). In Figure 3, the percentages of variance explained by the
Computer Experience
Rho = 0.08 *
Rho = 0.13 **
50.5% Perceived Usefulness
28.0% Usage Frequency Rho = 0.66 ***
74.4%
Job Category
Attitude Toward Using
Rho = 0.67 ***
Rho = 0.16 **
System Experience
model for each of the five predicted variables is indicated above that variable. Path coefficients that are not significant at the 90% confidence level are not presented.
7.6%
Rho = 0.26 ***
Perceived Ease of Use Rho = 0.19 **
Rho = 0.53 ***
Rho = 0.45 ***
20.0% Usage Amount
* Significant at p < 0.10 ** Significant at p < 0.05 *** Significant at p < 0.001
Figure 3: Measurement model results. One purpose of this study is to examine the direct and indirect influences of the external variables on usage behavior. TAM asserts that the influence is always indirect as mediated by the belief constructs. To investigate this notion, the empirical research model was revised to introduce direct links between each of the external variables and attitude, usage frequency and usage amount. In this revised model, two of the direct links, one between system experience and usage frequency, and another between system experience and usage amount, were found to be significant at the 95% confidence level. None of the remaining direct links were significant. Table 4 presents fit measures for the structural equations in the revised model. Revised measurement model path coefficients are presented numerically in Table 5 and graphically in Figure 4. In Figure 4, the percentage of variance explained by the revised model for each of the five predicted variables is indicated above that variable. Notice that the percentages of usage frequency and usage amount variances explained increased from 28.0% (Figure 3) to 39.9% (Figure 4), and from 20.0% (Figure 3) to 37.8% (Figure 4), respectively.
Table 4: Fit measures for the revised model. Recommended Fit Values Measures Chi-Square p > 0.05 p < 0.01 Chi-Square/DF < 3.0 1.98 Goodness of Fit > 0.90 0.85 Adjusted GFI > 0.80 0.81 Bollen's Index > 0.90 0.99 RMSR < 1.0 0.34 Table 5: Revised model path coefficients. Path Coefficient Rho (Comp. Exp. to usefulness) 0.10* Rho (Job category to usefulness) 0.17* Rho (Job category to ease of use) 0.14* Rho (System exp. to usage freq.) 0.40** Rho (System exp. to usage amount) 0.40** Rho (Ease of use to usefulness) 0.57** Rho (Usefulness to attitude) 0.67** Rho (Ease of use to attitude) 0.21* Rho (Attitude to usage frequency) 0.47** Rho (Attitude to usage amount) 0.46** * Significant at 0.05 ** Significant at 0.001
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38.6% Rho = 0.10 *
Computer Experience Job Category Rho = 0.14 *
Perceived Usefulness
39.9% Usage Frequency Rho = 0.67 **
67.2% Rho = 0.17 *
Attitude Toward Using
Rho = 0.57 **
2.0% Perceived Ease of Use
Rho = 0.47 ** Rho = 0.46 **
Rho = 0.40 **
System Experience Rho = 0.40 **
37.8% Usage Amount
Rho = 0.21 *
* Significant at p < 0.05 ** Significant at p < 0.001 Figure 4: Revised measurement model results.
5: Discussion In terms of the original TAM belief and attitudinal constructs and their empirical relationships to usage, this study largely confirms the findings of previous studies. Davis’ [7] original study reported ease of use to be a causal antecedent of usefulness. Subsequently, Davis [8] reported ease of use to have a direct effect on usefulness, and a smaller effect on attitude. Davis [8] also reported that usefulness had an expected, significant effect on attitude. The findings of this study are similar. Tables 3 and 5 present the standardized path coefficients from the tested models. The comparative magnitudes of corresponding path coefficients linking beliefs to attitudes to usage are relatively unchanged. That is, the effects of ease of use on usefulness, ease of use on attitude, usefulness on attitude, and attitude on usage frequency and amount are similar in Figures 3 and 4. Thus, revising the model with respect to the external variables did not affect the relative magnitudes of the TAM predictor upon the predicted variables. However, revising the model with respect to the external variables did impact the relative proportions of usage frequency and usage amount variances explained. This finding is important because the purpose of TAM is to predict the user acceptance of new information technologies, specifically by predicting user behaviors with those technologies. Thus, models that explain, or account for, larger proportions of usage behavior are
inherently more valuable. Figures 3 and 4 show the percentages of explained variances, for each of the predicted TAM variables, from the two models. The revised model (Figure 4) accounts for more variance in ‘how often’ (usage frequency) and ‘how much’ (usage amount) the application is used.
6: Conclusions The results of this study suggest the need to further examine the role that external variables play in predicting usage behaviors. For emerging information technologies to be used effectively in an organizational setting, there must be a fit between technology and task and between individual characteristics and the technology. The crux of perceived usefulness relates to the functionality of the application as enabling and expediting task-related job performance. To be perceived as useful, the functionality of an application must enable the user to accomplish job-related tasks. However, the perceived usefulness of an application is also promoted by the lack of difficulty (e.g. perceived ease of use) in using that application. For the individual to find an application as easy to use, there must be some consistency between the action language (e.g. what the user can do to the application) and the presentation language (e.g. how the application communicates to the user). Clearly, standardized user interfaces promote ease of use, but training and education are also important, as
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are other individual variables (e.g. age, gender, intrinsic cognitive skills) that cannot be so easily manipulated. Our findings validate the notion that beliefs and attitudes are instrumental in promoting the user acceptance of new information technologies. But external variables, both individual and organizational, are also an important consideration with respect to the process of adopting new information technologies. Both the indirect and the direct effects of these external variables on user behavior need to be considered.
7: References [1] Adams, D.A., Nelson, R.R. and Todd, P.A. (1992). Perceived usefulness, ease of use and usage of information technology: A replication. MIS Quarterly, 16.3, 227-247. [2] Ajzen, I. and Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall. [3] Bailey, J.E. and Pearson, S.W. (1983). Development of a tool for measuring and analyzing computer user satisfaction. Management Science, 29.5, 530-545. [4] Benbasat, I. (1990). Laboratory experiments in information systems studies with a focus on individuals: A critical appraisal. In Izak Benbasat, Ed. The Information Systems Research Challenge: Experimental Research Methods. Vol. 2. pp. 37-47. Boston, MA: Harvard Business School. [5] Bollen, K.A. (1989). Structural Equations With Latent Variables. New York: Wiley. [6] Davis, F.D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. doctoral dissertation, MIT Sloan School of Management, Cambridge, MA. [7] Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13.2, 319-339. [8] Davis, F.D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of ManMachine Studies, 38, 475-487. [9] Davis, F.D., Bagozzi, R.P. and Warshaw, P.R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35.8, 982-1003. [10] Davis, F.D. and Venkatesh, V. (1995). Measuring user acceptance of emerging information technologies: An assessment of possible method biases. Proceedings of the 28th Annual Hawaii International Conference on Systems Sciences, Maui, Hawaii, IV, 729-736. [11] Davis, S.A. and Bostrom, R.P. (1993). Training end users: An experimental investigation of the roles of the computer interface and training methods. MIS Quarterly, 17.2, 61-85. [12] Delone, W.H. and McLean, E.R. (1992). Information system success: The quest for the dependent variable. Information System Research, 3:1, 60-95.
[13] Dickson, G.W., Benbasat, I. and King, W.R. (1980). The management information systems area: Problems, challenges and opportunities. Proceedings of the First International Conference on Information Systems, 1-7. [14] Doll, W.J. and Torkzadeh, G. (1988). The measurement of end-user computing satisfaction. MIS Quarterly, 12.2, 259-274. [15] Fishbein, M. and Ajzen, I. (1977). Attitudes and opinions. Annual Review of Psychology, 23, 487-544. [16] Fishbein, M. and Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, Massachusetts: Addison-Wesley. [17] Hartwick, J. and Barki, H. (1994). Explaining the role of user participation in information system use. Management Science, 40.4, 440-465. [18] Hendrickson, A.R., Massey, P.D. and Cronan, T.P. (1993). On the test-retest reliability of perceived usefulness and perceived ease of use scales. MIS Quarterly, 17.3, 227-230. [19] Hubona, G.S. (1995). Evaluating user interface design with belief constructs. Proceedings of the Twenty-eighth Annual Hawaii International Conference on System Sciences, Maui, Hawaii. [20] Hubona, G.S. and E. Kennick. (1996). The impact of external variables on information technology usage behavior. Proceedings of the Twenty-ninth Annual Hawaii International Conference on System Sciences, Maui, Hawaii, IV, 166-175. [21] Ives, B. and Olson, M.H. (1984). User involvement and MIS success: A review of research. Management Science, 30.5, 586-603. [22] Ives, B., Olson, M.H. and Baroudi, J.J. (1983). The measurement of user information satisfaction. Communications of the ACM, 26.10, 785-793. [23] Jarvenpaa. S., Dickson, G.W. and DeSanctis, G. (1985). Methodological issues in experimental IS research: Experiences and recommendations. MIS Quarterly, 9.2, 141-156. [24] Joreskog, K.G. and Sorbom, D. (1989). LISREL 7 User's Reference Guide. Chicago, IL: Scientific Software Inc. [25] Keen, P. (1980). MIS research: Reference disciplines and a cumulative tradition. Proceedings of the First International Conference on Information Systems, Philadelphia, PA, 9-18. [26] Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2.3, 173-191. [27] Moore, G.C. and Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2.3, 192-222. [28] Segars, A.H. and Grover, V. (1993). Re-examining perceived ease of use and usefulness: A confirmatory factor analysis. MIS Quarterly, 17.4, 517-525. [29] Subramanian, G.H. (1995). A replication of perceived usefulness and perceived ease of use measurement. Decision Sciences, 25, 863-874. [30] Venkatesh, V. and Davis, F.D. (1994). Modeling the determinants of perceived ease of use. Proceedings of the
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Fifteenth International Conference on Information Systems, Vancouver, B.C., 213-227. [31] Wheaton, B.B., Muthen, B., Alwin, D.F. and Summers, G.F. (1977). Assessing reliability and stability in panel
models. In D.R. Heise, Ed. Sociological Methodology 1977. Ed. San Francisco: Jossey-Bass.
8: Appendix Subjects responded to the following questions by marking an “X” in the center of one of the seven places indicated for each question: 1. Using cc:mail in my job enables me to accomplish tasks more quickly: likely ________ : _______ : _______ : _______ : _______ : _______ : _______ unlikely extremely quite slightly neutral slightly quite extremely 2. Using cc:mail improves my job performance: likely ________ : _______ : _______ : _______ : _______ : _______ : _______ unlikely extremely quite slightly neutral slightly quite extremely 3. Using cc:mail in my job increases my productivity: likely ________ : _______ : _______ : _______ : _______ : _______ : _______ unlikely extremely quite slightly neutral slightly quite extremely 4. Using cc:mail enhances my effectiveness on the job: likely ________ : _______ : _______ : _______ : _______ : _______ : _______ unlikely extremely quite slightly neutral slightly quite extremely 5. Using cc:mail makes it easier to do my job: likely ________ : _______ : _______ : _______ : _______ : _______ : _______ unlikely extremely quite slightly neutral slightly quite extremely 6. I find cc:mail useful in my job: likely ________ : _______ : _______ : _______ : _______ : _______ : _______ unlikely extremely quite slightly neutral slightly quite extremely 7. Learning to operate cc:mail was easy for me: likely ________ : _______ : _______ : _______ : _______ : _______ : _______ unlikely extremely quite slightly neutral slightly quite extremely 8. I find it easy to get cc:mail to do what I want it to do: likely ________ : _______ : _______ : _______ : _______ : _______ : _______ unlikely extremely quite slightly neutral slightly quite extremely 9. My interactions with cc:mail are clear and understandable: likely ________ : _______ : _______ : _______ : _______ : _______ : _______ unlikely extremely quite slightly neutral slightly quite extremely 10. I find cc:mail to be flexible to interact with: likely ________ : _______ : _______ : _______ : _______ : _______ : _______ unlikely extremely quite slightly neutral slightly quite extremely 11. It was easy for me to become skillful at using cc:mail: likely ________ : _______ : _______ : _______ : _______ : _______ : _______ unlikely extremely quite slightly neutral slightly quite extremely 12. I find cc:mail easy to use: likely ________ : _______ : _______ : _______ : _______ : _______ : _______ unlikely extremely quite slightly neutral slightly quite extremely 13. All things considered, my using cc:mail in my job is: good ________ : _______ : _______ : _______ : _______ : _______ : _______ bad extremely quite slightly neutral slightly quite extremely 14. All things considered, my using cc:mail in my job is: wise ________ : _______ : _______ : _______ : _______ : _______ : _______ foolish extremely quite slightly neutral slightly quite extremely 15. All things considered, my using cc:mail in my job is: favorable ________ : _______ : _______ : _______ : _______ : _______ : _______ unfavorable extremely quite slightly neutral slightly quite extremely 16. All things considered, my using cc:mail in my job is: beneficial ________ : _______ : _______ : _______ : _______ : _______ : _______ harmful extremely quite slightly neutral slightly quite extremely 17. All things considered, my using cc:mail in my job is: positive ________ : _______ : _______ : _______ : _______ : _______ : _______ negative extremely quite slightly neutral slightly quite extremely
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