Student’s acceptance and use of online registration systems in South African universities

PROCEEDINGS OF THE 12th ANNUAL CONFERENCE ON WORLD WIDE WEB APPLICATIONS 21-23 September 2010 Durban South Africa Editors: A. Koch P.A. van Brakel

Publisher: Cape Peninsula University of Technology PO Box 652 Cape Town 8000

Proceedings published at http://www.zaw3.co.za

ISBN: 978-0-620-48797-9

2

TO WHOM IT MAY CONCERN The full papers were refereed by a double-blind reviewing process according to South Africa‟s Department of Higher Education and Training (DHET) refereeing standards. Before accepting a paper, authors were to include the corrections as stated by the peerreviewers. Of the 73 full papers received, 55 were accepted for the Proceedings (acceptance rate: 75.3%). Papers were reviewed according to the following criteria:      

Relevancy of the paper to Web-based applications Explanation of the research problem & investigative questions Quality of the literature analysis Appropriateness of the research method(s) Adequacy of the evidence (findings) presented in the paper Technical (eg language editing; reference style.

The following reviewers took part in the process of evaluating the full papers of the 12th Annual Conference on World Wide Web Applications, held from 21 to 23 September 2010 in Durban, South Africa: Mr U Averweg eThekwini Municipality / University of KwaZulu-Natal Durban Prof J Brits Faculty of Information Sciences (Dean) University of Wisconsin Milwaukee USA Prof A Bytheway Research Unit Faculty of Informatics and Design Cape Peninsula University of Technology Dr W Chigona Department of Information Systems University of Cape Town Cape Town Dr AM El-Sobky Educational Sciences RITSEC Cairo Egypt

th

Proceedings of the 12 Annual Conference on World Wide Web Applications, Durban, 21-23 September 2010 (http://www.zaw3.co.za) ISBN: 978-0-620-48797-9

3

Prof M Herselman Living Labs Meraka Institute CSIR Pretoria Prof S Mutula Department of Information Science University of Botswana Botswana Dr Z Mitrovic Department of Information Systems Faculty of Management University of the Western Cape Cape Town Dr David Raitt European Space Agency The Hague The Netherlands Prof A Singh Department of Business Information Systems University of KwaZulu-Natal Durban Prof P Weimann Economics and Social Sciences University of Applied Sciences Berlin Germany Further enquiries: Prof PA van Brakel Conference Chair: Annual Conference on WWW Applications Cape Peninsula University of Technology Cape Town +27 21 469 1015 (landline) +27 82 966 0789 (mobile)

th


4

Student’s acceptance and use of online registration systems in South African universities B M Kalema Department of Informatics, Tshwane University of Technology Pretoria, South Africa [email protected] R M Kekwaletswe Department of Informatics, Tshwane University of Technology Pretoria, South Africa [email protected]

Abstract The objective of this paper is to establish the factors that influence students‟ acceptance and use of online registration system in a South African university. This study used the Unified Theory of Acceptance and Use of Technology (UTAUT). The study is informed by empirical evidence of a case study of Tshwane University of Technology (TUT), with survey sample of 300 students from four campuses: Pretoria, Shoshanguve, Garankuwa and Polokwane. The analysis of results shows that much as there is no significant correlation between performance expectancy and behavior intention, the constructs‟ reliability shows that UTAUT is a good model for predicting acceptance and use of online registration system. However this study recommends that the constructs of UTAUT need to be modified in order to have a better prediction model. The study contributes to the literature of students‟ acceptance, adoption and use of web-based tools in the academic arena. Keywords: Online registration, web-based tools, e-registration, unified theory of acceptance and use of technology (UTAUT), acceptance and use of technology. 1.

Introduction

The current trend in South African education environment has made institutions of higher learning to entirely depend on information and communication technology (ICT) for their day to day operations. Changes like increasing number of students, merging of campuses has exponentially changed the demographic structure of universities, nature of activities and have negatively impacted on the service delivery. This calls for effective use of information technology (IT) in order to turn the burden of burgeoning business data into a bounty of business opportunity. The ubiquity of internet has provided a gateway to international competition and differentiation. Hence, universities have ended up investing good sums of money in IT so as to improve on their effectiveness in service delivery hoping to achieve a good market and competitive advantage. th


5

One such management information system that has been widely acquired by universities in South Africa is the integrated tertiary software (ITS). ITS is designed with the student iEnabler component that enhances students interaction with the university‟s functional systems. With the student iEnabler, students can apply, make payments, register, and do online consultation. The online registration system‟s major intention is to reduce the overcrowding of students during the time of registration which is normally at the beginning of the semester. If used effectively this system should save the students from the time they wait in lines and also reduce the burden of university staff getting overworked during these periods. As students wait impatiently in these long queues, they become loudly hence causing a state of un calmness that in most cases ends up into riots. The institution‟s aim of introducing such systems is to avoid such nasty incidents. However, much as web-based tools have become popular of late with the advent of face book, yahoo messenger and other chatting web tools prompting many teenagers to visit and use the web, research (Wei He & Trudi, 1996) shows that education based tools have not been fully utilized. Yet institutions that invest in IT can only see its importance if it is accepted and fully utilized. The objective of this paper is to develop a conceptual framework for measuring university students‟ acceptance and use of online-registration system using the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003). This study is expected to contribute; theoretically on the literature of acceptance and use of web-based tools and; practically the framework will be used by universities in making informed decision when investing in web-based tools. 2.

Literature review and research model

After comparing eight original models and theories of acceptance and use, Venkatesh et al. (2003) unified them and came up with UTAUT. The theories that were unified included the Theory of Reasoned Action (TRA) (Fishbein & Ajzen, 1975), Technology Acceptance Model (TAM) (Davis,1989), Motivational Model (MM) (Davis et al., 1992), Theory of Planned Behavior (TPB) (Ajzen, 1991), Model Combining the Technology Acceptance Model and Theory of Planned Behavior (C-TAM-TPB) (Taylor & Todd , 1995), Model of PC Utilization (MPCU) (Triandis, 1980; Thompson et al., 1991), Innovation Diffusion Theory (IDT) (Rogers, 1962; Benbasat, 1991), and Social Cognitive Theory (SCT) (Bandura, 1986; Compeau & Higgins, 1995). Researchers (PuLi, & Kishore, 2005; Anderson & Schwager, 2004) who have evaluated UTAUT and those who have extended it (Cody-Allen & Kishore, 2006; Tibenderana & Ogao, 2008), have found its constructs very predictive. A critical review of UTAUT‟s constructs clearly indicates that users‟ intention to use information technology (IT) can be determined by three constructs: performance expectancy, effort expectancy, social influence. As a result, users‟ intention to use influences their actual behavior towards IT adoption with facilitating conditions (Venkatesh et al. 2003).

th


6

Another underlying model that has been very significant in the studying of users‟ acceptance and use of technology is the technology acceptance model (TAM) (Davis, 1989), TAM emphasizes that users‟ acceptance of technology is highly influenced of their perception of ease of use and the usefulness they would expect from the technology. However, this may be non-significant over extended and sustained usage putting in mind that users may accept a system at the beginning but as they continue using it many factors accrue and they develop biasness over the system. This affects the perceived usefulness, hence for such systems like online registration which are periodical but continuous care should be taken to determine their acceptance and usage rather than acceptance only. That is why this study opted for UTAUT. Figure1: The UTAUT Model (Venkatesh et al., 2003)

Performance Expectancy

Use Behavior

Behavioral Intention

Effort Expectancy Social influence Facilitating Condition

Age

Gender

Experienc e

Voluntariness Use

The UTAUT model figure 1, indicates that its construct „performance expectance‟ is moderated by gender and age. This implies that people of a given age and a particular sex will to a certain degree believe that using a given technology will help them get a better performance or usefulness. Whereas the degree of ease that may be associated with a given technology, „effort expectancy‟ is moderated by gender, age, and experience in using related technology. On the other hand, the model‟s construct „social influence‟ or the degree to which individual believes that other important people in their lives would influence them to use the technology is moderated by all the four factors; age, gender, experience in using related technology and voluntariness of use. And finally, the availability of resources „facilitating conditions‟ is being moderated by age and experience in using related technologies. It is this underlying principle that this paper adopted when carrying out this study. 3.

Methodology

th


of

7

Primary data for this study was collected by using close-ended questionnaires based on seven-point Likert scale: strongly disagree (1), disagree (2), slightly disagree (3), neither disagree nor agree (4), slightly agree (5), agree (6) and strongly agree (7). A case study of Tshwane University of Technology (TUT) was used with data collected from four campuses; Pretoria, Shoshanguve, Ga-rankuwa and Polokwane. A total of 300 students participated. The questionnaire designed was based on the instrument developed by Venkatesh, et. al. (2003). 4.

Data analysis

Questionnaire data was analyzed by using the statistical package for social scientists (SPSS). This package was used because it could well establish the reliability of the model and also give an effective descriptive statistical analysis of the data. 4.1

Limitations

The researcher depended mostly on lecturers to distribute questionnaires to students shortly before or after the lecture. Since a lecturer‟s presence may influence a student‟s response to the questionnaire, students‟ response in this case may not actually represent his/her exact feelings. Further still, 30% of the respondents were National Diploma students whose awareness and experience with web-based tools is limited putting in mind that many come from less privileged backgrounds. Therefore, the results in this study may not accurately represent all students in South African universities or students from developing world. 4.2

Results

Table 1 below shows the sample characteristics of the participants. It is the demographic summaries of the participants‟ settings as extracted from the questionnaire. 4.2.1 Demography of the participants Table 1: Demographic data of the respondents (N = 300)

SAMPLE CHARACTERISTICS Investigated Factors

Content

Soshanguve

th

RESULTS Number of Respondents

Percentage %

194

64.7


8 Campuses

Faculty in which the student is registered

Course or level of study

Pretoria

20

6.7

Garunkuwa

28

9.3

Polokwane

58

19.3

ICT

223

74.3

Science

12

4

Education

10

3.3

Economics and finance

47

15.7

Engineering and Built technology

8

2.7

National Diploma

90

30

BTech

180

60

MTech

24

8

DTech

6

2

80

26.7

Fulltime and working

32

10.7

Weekend and working

160

53.3

Weekend not working

20

6.7

Part – time working

8

2.7

17-19

25

8.3

20-22

26

8.7

23-25

40

13.3

26-28

56

18.7

29-31

58

19.3

32-34

69

23

34+

26

8.7

Male

118

39

Female

182

61

Google chat

45

15

Face book

208

69.3

Internet Banking

21

7

Online payment systems

8

2.7

Online registration systems

68

26.7

e-business

10

3.3

Never used any

100

33.3

146

48.7

Fulltime not working

Student’s Status

Age group

Gender

Web-based tools used before

Awareness of e-registration

Ever heard of eregistration before

th


9 Never heard of eregistration before

154

51.3

4.2.2 Descriptive statistics Before the validity of constructs is determined it is important to first establish the descriptive statistics for each construct and its corresponding items as indicated in table2 below. Table 2: Descriptive statistics

SCALES / ITEMS

MEAN

S.D.

Performance Expectancy (PE)

22.72

4.61

PE1: I find online registration useful in my registration.

6.22

1.17

PE2: Using online registration enables me to accomplish registration more quickly.

5.61

1.30

PE3: Using online registration increases my understanding of registration.

5.54

1.27

PE4: Using online registration increases my chances of registering on time.

5.35

1.45

Effort Expectancy (EE)

20.54

6.43

EE1: My interaction with online registration system is clear and understandable.

4.72

1.89

EE2: It is easy for me to become skillful at using online registration system.

5.56

1.73

EE3: I find online registration system easy to use.

5.07

1.92

EE4: Learning to operate online registration system is easy for me.

5.19

1.79

Social Influence (SI)

19.02

5.93

SI1: People who influence my behavior think that I should use online registration system.

4.34

1.84

SI2: People who are important to me think that I should use online registration system.

4.95

1.86

SI3: Lecturers in my classes have been helpful in the use of online registration system.

5.07

2.03

SI4: In general, the university has supported the use of online registration system.

4.66

2.18

Facilitating Conditions (FC)

18.55

5.06

FC1: I have the resources necessary to use online registration.

4.88

2.27

FC2: I have the knowledge necessary to use online registration.

5.21

2.10

FC3: Online registration system is not compatible with other systems I use.*

3.86

1.92

FC4: A specific person (or group) is available for assistance with online registration difficulties.

4.61

2.01

th


10

Behavioral Intention to Use the System (BI)

16.91

3.48

BI1: I intend to use online registration in the next semesters.

5.25

1.27

BI2: I predict I would use online registration in the next semesters.

6.07

1.16

BI3: I plan to use online registration in the next semesters

5.69

1.15

Note: * denotes a reversed scale.

From the descriptive statistics table 2 above, the high mean rate indicates that many students acknowledged that using online registration system improves performance expectance. However the lower mean rate for facilitating conditions many students 51.3% expressed ignorance or limited knowledge of online registration system. Limited awareness of students may have come from the fact that TUT‟s online registration system is a new tool that may have not been well advertised or that the many interviewed students were new at the university since the survey was carried out in the early period of semester1. On the other hand students‟ responses showed little relation between social influence and usage of online registration system. This may have been attributed from the fact that over 40% of the respondents were above the age of 32 years and over 53% are working students, meaning this group of students has little or no time of interaction with other students as they only attend lectures over the weekend. 4.2.3 Validation and reliability Cronbach, (1951) proposed “alpha coefficient” that may be used to test the reliability of the construct or the degree to which the selected construct instrument items can measure that construct. This shows the strength of the model but it is also important to note that the collected data may highly influence this reliability. Results in table 3 indicate that apart from facilitating condition that has a Cronbach alpha of .433 which is below the recommended 0.7, the rest of the construct shows a good reliability of the UTAUT model to measure students‟ acceptance and use of online registration system. Though social influence has a relatively lower Cronobach alpha 0.736, it a reliable variable to determine behavioral intention. Table 3: Alpha coefficient showing the reliability of constructs

CONSTRUCTS

QUESTIONS PER CONSTRUCT

CRONBACH’S ALPHA

Performance expectance (PE)

4

0.902

Effort Expectancy (EE)

4

0.901

Social Influence (SI)

4

0.736

Facilitating Condition (FC)

4

0.433

Behaviour Intention (BI)

3

0.978

th


11

4.2.4 Correlation of results UTAUT model figure1, shows that behavior intention (BI) depends on the variation of the constructs performance expectancy (PE), effort expectancy (EE) and, social influence (SI) also confirmed by results in table3. It is therefore important, to determine how these independent variables (PE), (EE) and (SI) correlate with the dependent variable (BI). However, much as UTAUT predicts significant correlation between performance expectancy and behavior intention, the study tested the results with both Spearman and Pearson and no significant correlation was established. Table 4: Correlation of constructs

PE1

PE2

PE3

PE4

EE1

EE 2

EE3

EE4

SI1

SI2

SI3

SI4

1

PE1 PE 2

.175

1

.180

PE3

PE4

EE1

EE2

EE3

EE4

.236

.266*

1

.070

.040

.287*

.346* *

.647**

.026

.007

.000

.210

-.015

.217

.277*

.113

.910

.102

.035

.159

.114

.003

-.005

.628* *

.233

.393

.982

.970

.000

.065

.126

.428**

.342**

.603* *

.596 **

.629

.350

.001

.009

.000

.000

.186

.105

.238

.206

.742* *

.801 **

1

1

th

1

1

.814* *

1


BI1

BI2

BI3

12

SI1

SI2

SI3

SI4

BI1

BI2

BI3

.166

.437

.075

.125

.000

.000

.000

.149

.307*

.063

.163

.304*

.380 **

.345* *

.430* *

.263

.019

.638

.220

.022

.004

.009

.001

.293*

.351* *

.298*

.352**

.117

.197

.140

.222

.452**

.025

.007

.023

.007

.387

.143

.303

.100

.000

.224

.077

.332*

.361**

.312*

.278 *

.366* *

.354* *

.181

.354**

.090

.567

.011

.005

.018

.036

.006

.007

.174

.006

.291*

.253

.315*

.370**

.240

.232

.305*

.279*

.292*

.415**

.741* *

.027

.056

.016

.004

.072

.082

.022

.037

.026

.001

.000

-.190

.012

.108

.220

.024

.040

.161

.025

-.081

-.094

.004

.143

.146

.927

.410

.092

.857

.765

.233

.854

.545

.482

.975

.283

.033

-.200

-.077

-.025

-.098

.064

-.087

-.145

.010

-.020

-.126

.211

-.100

.802

.125

.557

.848

.465

.632

.519

.283

.941

.883

.344

.113

.445

.148

-.007

.179

-.097

-.034

.194

.003

-.064

-.007

.061

.114

.138

-.289*

.045

.258

.960

.171

.461

.801

.145

.982

.638

.958

.647

.393

.301

.025

.733

1

1

1

1

1

1

* Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed). 5.

Conclusion and recommendations

This study investigated the use of UTAUT for acceptance and use of online registration. UTAUT‟s constructs were tested with empirical data collected from the survey. Much as the results don‟t show significant correlation between the independent constructs; performance expectancy, effort expectancy, social influence and the dependent construct behavior intention, there is a degree of relation between the two. This is so because the Cronobach alpha of these constructs was all in the acceptable range of 0.7 th


1

13

and above showing the validity of the model in establishing of the acceptance and use of online registration. This study recommends that, in related studies the effect of the moderating factors on the constructs should be established as awareness, course of study and experience with web-based tools is likely to cause a big influence. This study also recommends that further research should sample students from several universities, so as to get a diverse view of students from different background. 6.

List of references

Ajzen, Icek. “The Theory of Planned Behavior,” Organizational Behavior and Human Decision Processes, 50 (1991): 179-211. Anderson, J. E. & Schwager, P. H. (2004). SME Adoption of Wireless LAN Technology: Applying the UTAUT Model. Proceedings of the 7th Conference of the Southern Association for Information Systems. Available. At: http://sais.aisnet.org/sais2004/Anderson%20&%20Schwager.pdf Accessed 20 March. 2009 Bandura, Albert. Social Foundations of Thought and Action: A Social Cognitive Theory, NJ: Prentice Hall, 1986. Benbasat, I, and Moore, G. C., (1991). “Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation,” Information Systems Research, 2 (1991): 192-222. Cody-Allen, E. & Kishore, R. (2006). An Extension of the UTAUT Model with E- Eqality, Trust & Satisfaction Constructs. CPR 2006:183-189. Available: http://portal.acm.org/citation.cfm?id=1125196. Accessed: 15 March 2009. Compeau, Deborah R., Higgins, Christopher A. “Computer Self-Efficacy: Development of a Measure and Initial Test,” MIS Quarterly, 19 (1995): 189-211 Cronbach, L. J. (1951). Coefficient Alpha and the Internal Structure of Tests. Psychometrika. , 16, 297-334. Davis, F. D.(1989). “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology,” MIS Quarterly, 13 (1989): 319-340. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111−1132. Fishbein, M. and Ajzen, I (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research, MA: Addision-Wesley Pu-Li, J. and R. Kishore, (2005). How Robust is the UTAUT Instrument? A Multigroup Invariance Analysis in the Context of Acceptance and Use of Online Community Weblog Systems. Proceedings of the 2006 ACM SIGMIS CPR conference on computer th


14

personnel research: Forty four years of computer personnel research: achievements, challenges & the future. Claremont, California, USA. Available: http://portal.acm.org/citation.cfm?doid=1125170.11252118 Accessed: April. 2009. Rogers, E. M(1962). Diffusion of Innovations, NY: Free Press,. Taylor , S. and Todd, P. A. (1995). “Assessing IT Usage: The Role of Prior Experience,” MIS Quarterly, 19 561-570. Tibenderana, P. K.G. and Ogao, P. J. (2008). Acceptance and Use of Electronic Library Services in Ugandan Universities Libraries. Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries. pp 323-332 ACM New York, NY, USA Thomson. R.L, Higgins, C. A., Howell, J. M (1991). “Personal Computing: Toward a Conceptual Model of Utilization,” MIS Quarterly, 15 (1991): 124-143 Triandis, H. C. (1980). “Values, Attitudes, and Interpersonal Behavior,” Nebraska Symposium on Motivation, 27 195-259. Venkatesh, V. and Davis F. D (2000). “A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies,” Management Science, 46 (186204. Venkatesh V., Morris M. G, Davis B. G, Davis D. F (2003).User Acceptance of Information Technology: Towards a Unified View. MIS Quarterly. MIS Research Center, University of Minnesota. Vol. 27, No. 3 pp 425-478. Available: http://www.jastor.org/stable/300336540. Accessed: 5th March 2010 Wei He , P. and Trudi E. J.,(1996).What Are They Doing with the Internet? A Study of User Information Seeking Behaviors,. Internet Reference Services Quarterly 1, no. 1 (1996): 31.51.

7.

Acknowledgments

The authors wish to thank the staff of TUT and the students who participated in the survey. Sincere thanks goes to Prof. O.O. Olugbara for guidance and support.

th


Student’s acceptance and use of online registration systems in South African universities

Recommend Documents