Rudnicka, P. (2008). The Psychology of Internet Activity (poster). XXIX International Congress of Psychology, 20-25.07.2008. Berlin, Germany.

1. THE PSYCHOLOGY OF INTERNET ACTIVITY
Patrycja Rudnicka, University of Silesia, Poland
Introduction Method
As the Internet becomes more popular and accessible new Procedure
issues of Internet research appear in field of psychology. The Set of paper-pencil questionnaires was distributed to participants in class. Participation was voluntary, only a few
internet activity and factors influencing it can be explored and resignations/refusals has been observed during research. Both verbal and written instruction were provided.
explained from two different perspectives. The first one is
macrolevel scale with studies explaining the role of Variables and their measures
Sampl
demographics, economical and cultural conditionings as well e Internet Self-efficacy - a set of person's beliefs about
as global usage trends in predicting the Interent use. On Studen his or her capability to perform an Internet-related task in
ts
(N = 12 of over 20 d
individual, microlevel scale, other factors, such as self- 88) ifferent a various situations (see also Durndell &Haag, 2002;
special
isations
efficacy, attitudes, or anxiety towards the Internet come to the Averag Eastin & LaRose, 2000; Sam, Othman &Nordin, 2005;
e
( M = 2 1 a g e : 2 1 y ea r
.49; SD so
fore. Their influence on perceiving and using the Internet are = 2.15) ld Tsai & Tsai, 2003, Wu & Tsai, 2006). Assessed using
852 (66
widely discussed, but there are still many questions %) wom Internet Self-Efficacy Measure (ISEM), 10-item
en and
Majorit 436 (34
unanswered. y of us %) men instrument based on Computer Self-Efficacy Scale
longer ers with
than 2 e
This poster presents results of research conducted in winter years ( xperience (CSEM) designed by Compeau and Higgins (1995).
n = 909
2006 among polish students. Based on data gathered from Majorit , 71%)
y of us
at hom er Attitudes toward the Internet - positive / negative
e (n = 1 s with the Inte
over 1000 students, cluster analysis and structural equation 023, 79 rnet ac
.4%). cess beliefs toward the Internet. Measured using Internet
modeling (SEM) were used to identify and estimate causal
Attitude Scale (IAS), a modified Computer Anxiety Scale
relationships between psychological factors and patterns of
(CAS), 20-item instrument designed by Nickell and
the Internet use.
Pinto (1986).
The goals of research were:
Computer Anxiety - a fear, discomfort or negative emotional response when using the computer
- description and analysis of internet use patterns
or anticipating such situation (Bozionelos, 2001; Heinssen, Glass, & Knight, 1987; Rosen & Weil,
- identification and exploration of group differences
1995). Measured using Computer Anxiety Rating Scale (CARS), 19-item scale designed by
- verification of theoretical model of attitudes, anxiety and self-
Heinssen, Glass and Knight (1987).
efficacy influence on internet activity.
Internet Anxiety - a fear, discomfort or negative emotional response toward the Internet
consequences are avoidance or limitation time spent on-line (see Barbeite & Weiss, 2004; Joiner,
Results
et all., 2005). Internet Anxiety Scale (Internetowa Skala Lêku, ISL), 18-item instrument, has been
Internet Usage Patterns Analysis
designed to measure anxiety toward the Internet (Rudnicka, 2007).
O1
Two-step cluster analysis has been used to identified different patterns
of Internet use. The centroids for each cluster shows Table 1. Internet Activity - assessment was based on the questions on internet practice, frequency of use
Table 1
Clusters’ centroids (average time on-line on daily and weekly basis), number and frequency of services (WWW, IRC,
chat, IM, P2P, FTP, Usenet News, on-line games) and activities used in a period of time (web
browsing, information searching, on-line shopping etc.)
O Exploration of demographic differences between clusters.
2
O Exploration of psychological differences between clusters using MANOVA.
3
Table 2
2
Fig. 1. Percentage distribution of females and males in clusters, ÷ (3, N = 1288) = 158.84; p < .001
Descriptive statistics for ISEM, IAS, CARS/ISL in clusters (N = 1288)
CARS/ISL is a composite variable because of high correlation, r(1286) =.,65; p <.,001
A multuvariate analysis of variance was conducted to assess if there were differences between 2
Fig. 2. Percentage distribution of fileds of study in clusters, ÷ (9, N = 1288) = 211.53; p < .001
self-efficacy, attitudes and anxietes level in case of four groups of users. A significant difference
was found, Wilks’ Ë = .794; F(9, 3120) = 34.56; p < .001; ç2 = .074.
For all variables differences between groups were significant (F = 76.62; p < .001), IAS (F = 52.10;
p < .001) oraz CARS/ISL (F = 72.23; p < .001) with coefficients inidicate high values, ç = .33 for IAS,
ç = .38 for CARS/ISL and ç = .39 for ISEM.
Theoretical Model Verification
Structural equation modeling (SEM) has been used to analyse the model. First the
Fig. 3. Percentage distribution of internet access place in clusters
measurement part of the model has been tested, then fit of the model using path analysis
was conducted.The fit statistics were all indicative for a reasonable model fit however Discussion
2 2
the ÷ statistic was significant (i.e. ÷ (308, N = 1188) = 856, p < .001, RMSEA = .039, AGFI
Different mechanisms of regulation and activity structure in four groups of users
= .937, CFI = .956).
presenting different patterns of usage were identified.
Systematic differences between groups were based on both demographic factors (like
->
gender, field of study, type of internet access) but also psychological factors (like level of
self-efficacy, anxiety or attitudes). Thus, digital inequality in case of Cluster 3 was caused
by technological exclusion, whereas in case of Cluster 4 a voluntary renounciation,
related to their psychological characteristics, was observed.
Based on SEM results the psychological factors impact on internet activity can be
- > explainded as follows:
- computer anxiety and low self-efficacy are most important factors influenced internet
activity,
- internet anxiety does not limit internet activity,
- attitudes are less important.
The theoretial model’s verification between four different groups of users was also
Fig. 4. Path coefficients for model conducted. For all groups the model adjustment was reasonable (RMSEA= .030-.048,
*p < .05, **p < .01, ***p < .001
AGFI = .865-.963, CFI = .921-.963, ÷2/df = 1.2-1.8). Then factors loadings equivalence
Most relations were in the hypothesized direction, but Internet anxiety was positively has been testing to check whether the same latent variables are measured. However
reacted to Internet activity and that direction was opposite to the hypothesized one. results failed, the structure of latent factors was not identical, ÷2(22) = 109.3, p < .001.
Regards to hypothesized correlations between individual factors only one failed to meet Tests of further hypothesis were no longer possible, and only the analysis of
significance criteria however all correlation signs have been confirmed. unstandardised solution has been conducted. The main differences were observed in
References latent construct of Internet Activity in case of memebers of Cluster 2.
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Bozionelos, N. (2001). The relationship of instrumental and expressive traits with computer anxiety. Personality and Individual Differences, 31(6), 955-974.
Compeau, D. R. & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211.
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Comm ions?
Nickell, G. S. & Pinto, J. N. (1986). The computer attitude scale. Computers in Human Behavior, 2, 301-306.
Rosen, L. D & Weil, M. M. (1995). Computer anxiety: A cross-cultural comparison of university students in ten countries. Computers in Human Behavior, 11(1), 45-64.
Rudnicka, P. (2007). Psychologiczne mechanizmy podejmowania aktywnoœci w Internecie / Psychological mechanisms of the Internet Activity. Unpublished doctoral thesis, University of Silesia, Katowice, Poland.
Emai ents?
Sam, H., Othman, A. E. A., & Nordin, Z. (2005). Computer self-efficacy, computer anxiety, and attitudes toward the Internet: A study among undergraduates in Unimas. Educational Technology & Society, 8(4), 205-219. l me!
Tsai, M. J. & Tsai, C. C. (2003). Information Searching Strategies in Web Based Science Learning: The Role of Self-Efficacy. IETI, 40(1), 43-50.
Wu, Y-T & Tsai, C. C. (2006). University Students' Internet Attitudes and Internet Self-Efficacy: A Study at Three Universities in Taiwan. CyberPsychology & Behavior, 9(4), 441-450.
XXIX International Congress of Psychology
Berlin, 20-25 July 2008