Educ Inf Technol DOI 10.1007/s10639-017-9635-9
An empirical investigation of campus portal usage Mohsen Saghapour 1 & Mohammad Iranmanesh 2 & Suhaiza Zailani 1 & Gerald Guan Gan Goh 2
Received: 25 August 2016 / Accepted: 21 July 2017 # Springer Science+Business Media, LLC 2017
Abstract This study has determined the determinants of the perceived ease of use and perceived usefulness and their influence on campus portal usage. A quantitative approach was employed, using a five-point Likert scale questionnaire, adapted from previous studies. Data were gathered through a survey conducted with 341 staff working in the University of Malaya and were analysed using the partial least squares technique. The results indicate that the ease of finding and ease of understanding have a positive impact on the perceived ease of use of the campus portal. Furthermore, this study also reported a significant positive effect of service quality, information, and process on perceived usefulness of the campus portal. Our study suggests that the perceived ease of use and usefulness are vital towards enhancing the usage frequency and volume of the campus portal. This study broadens the knowledge concerning factors that increase campus portal usage which will enable academic institutions to refine the campus portal thus enhancing the portal usage. Keywords Campus portal . Academic institution . Portal usage . IS success model
* Mohammad Iranmanesh
[email protected] Mohsen Saghapour
[email protected] Suhaiza Zailani
[email protected] Gerald Guan Gan Goh
[email protected]
1
Faculty of Business and Accountancy, University of Malaya, 50603 UM, Kuala – Lumpur, Malaysia
2
Faculty of Business, Multimedia University, 75450 MMU, Melaka, Malaysia
Educ Inf Technol
1 Introduction The emergence of web-based technologies and the ensuing staff portals has caused drastic advances towards the method used by university staff to communicate, share knowledge, and manage information, as well as secure work processes. A portal, in general, is a gateway to information and services from multiple sources (Manouselis et al. 2009) that facilitate users’ access to the content in one or more learning repositories (Mphidi and Snyman 2004). The database in a portal supports users in a simple and centralised access to locate, retrieve, and store learning sources from various pertinent network content and applications (Manouselis et al. 2009). The staff portal, also known as the campus portal, in many academic institutions, which is developed from a collection of static web pages, has made a significant transformation into highly integrated and interactive information systems (IS) over the decades. Furthermore, the present campus portals which are mainly built based on a Bsingle point of access^ enables the front-end communication, applications, knowledge sharing, integration of information, and work processes within the corporations comparable to the earlier portals that only supplied an easy interface to information. Technically speaking, a campus portal provides access to resources, applications, and personalised information through a browser-based user interface whereby employees use the staff portal as the primary tool in their work (Tojib et al. 2006). In recent years, numerous academic institutions, especially large ones, offer their staff a portal. Development and maintenance of a campus portal is expensive and timeconsuming with a high failure risk (Bringula and Basa 2011; Urbach et al. 2010). If the staffs do not make full use of the portal, then the investment becomes a waste. Therefore, it is imperative that the campus portal needs to be designed in accordance to achieve its optimum use. The previous studies on the campus portal investigated the factors that affect user satisfaction and intention to use the campus portal (e.g., Lee et al. 2009; Bakar et al. 2014). However, previous studies have shown that the overlap between intention to use and actual usage is limited and it is inappropriate to take intention to use as a surrogate of usage behaviours (Wu and Du 2012; Zhang et al. 2012). Therefore, investigating the determinants of the campus portal’s usage volume and usage frequency, which is lacking in the literature, is important to achieve its optimum use. As such, the first objective of this study is to provide a conceptual framework that explains the factors that determine the usage volume and usage frequency of the campus portal. In particular, we have adopted the Technology acceptance model (TAM) as our theoretical base. Additionally, Legris et al.’s (2003) meta-analysis of TAM literature showed that external variables received little attention. The authors argued that it is important to study external variables because they are the ultimate drivers of usage. Therefore, the second objective of this study is to extend the TAM by considering the external variables that may have an effect on perceived ease of use and perceived usefulness and, consequently, lead to actual usage. The findings of the study would help the academic institutions to refine the current campus portal to meet the requirements of the staff. On the other hand, this study would add to the small number of empirical studies on campus portals (e.g., Bringula and Basa 2011; Bakar et al. 2014). Subsequent to the introduction, a literature review on the campus portals, technology acceptance model, and information success model are provided. A conceptual model is
Educ Inf Technol
proposed to determine the relationships between the usages of the campus portal with external variables and an empirical study is developed to validate the proposed conceptual model, and then followed by a discussion of the results and contributions. Finally, the limitations of the study are stated.
2 Literature review 2.1 Campus portals An early definition of a portal appeared in a Merill Lynch report (Shilakes and Tylman 1998). In this report, the information of the company was primarily incorporated in an application considered as a portal and a single interface to the information was provided for users. Subsequent definitions tend to include the integration of collaborative applications, such as e-mail and calendars (Eckerson 1999). At the present time, processes and applications are considered besides the integration of simple tools and information in the portals (Chan and Liu 2007; Daniel and Ward 2005) and as reflected in many different publications, the perception of portals has transformed over time (e.g., Ainin et al. 2012; Bringula 2013; Lee and Kim 2009; Urbach et al. 2010; and Bhuasiri et al. 2012). In present days, portals enable the front-end integration of applications, communication, information, and processes in a more compatible way than in the past whereby portals have evolved from low-end intranets into a highly integrated IS. Exploring the design of a portal, it incorporates functionalities and tools that support an organisation’s knowledge management which means the portal supplies tools for exchange and knowledge sharing, together with information and knowledge management activities, such as access, search, knowledge capture, dissemination, integration, and retrieval (Al-Busaidi 2012). According to Detlor (2000), a portal provides employees with a rich, shared information work space to create, exchange, store, retrieve, share, and reuse knowledge. A staff portal contains communication space for conversation, content space for information access and retrieval, and coordination space to support cooperative work tasks. Similarly, Aneja et al. (2000) illustrated that the organisations’ knowledge management activities could be supported by integrating the internal information resources involving data warehouses, organisational knowledge bases, documents, collaboration products and internal web sites, and external information resources involving external services, news and news feeds, external content and external web sites. Thus, a portal can provide a major support for academic institutions, the most intensive knowledge-based organisations. The integration capability of the campus portal enables linking internal databases (e.g., Courses, publications, library, etc.) and external information resources (such as e-journals, book publishers, etc.) to improve and smooth the core business processes (education, research, and consultancy). Collaboration tools in campus portals enrich knowledge exchange amongst the main users (students, academic, research and administrative staff, alumni, and industrial partners) of an academic institution. The body of literature on campus portals has consisted of three main streams from the management perspective. The first research flows are concerned with the benefits of
Educ Inf Technol
the campus portal (Al-Busaidi 2012, 2010). Al-Busaidi (2012) showed that a campus portal has significant returns on employees’ learning, adaptability and job satisfaction, and processes’ effectiveness, efficiency, and innovation. The second research flows investigate the usage of the campus portal. For example, Mphidi and Snyman (2004) found that the benefits of the campus portal are not utilised fully. Concerning the benefits of the campus portal and the low usage of these benefits, the third research flows concentrate on the factors that have an effect on the satisfaction with and the intention to use the campus portal (Ainin et al. 2012; Bakar et al. 2014; and Masrek 2007). As it is not appropriate to take satisfaction and intention to use instead of actual use (Wu and Du 2012; Zhang et al. 2012), this study extends the literature by considering usage volume and usage frequency as dependent variables. 2.2 Technology acceptance model In the past decade, the TAM has been considered significantly by researcher in the IS field. This model endeavors to explain and predict the use of the system to put forward perceived usefulness and perceived ease of use, which are two main determinants of IS acceptance (Fig. 1). The former is defined as Bthe degree to which a person believes that using a particular system would enhance his or her job performance^ and the latter is defined as Bthe degree to which using the technology will be free of effort^ (Davis 1989, p. 320). Both the perceived usefulness and perceived ease of use have an impact on the individual’s attitude towards using a system. PU and Attitude successively predict the individual’s behavioural intention using a system meanwhile perceived ease of use likely influences perceived usefulness. Therefore, the user interface improvement has a high impact on perceived usefulness and IS acceptance. Furthermore, both types of beliefs are subject to the effects of external variables. For instance, predicting the acceptance of web sites with the TAM application is considered by Lin and Lu (2000). The external variable includes IS quality, involving system accessibility response time, and information quality. The results show that the PU and PE of web sites have been significantly affected by these critical external variables. Therefore, users’ beliefs about the system, and then, their behavioural intentions and system use could be better controlled through system developers by manipulating them.
Fig. 1 Original TAM (Source: Davis 1985)
Educ Inf Technol
However, a meta-analysis of TAM literature identified that two aspects of the TAM, namely, the role of different usages and the role of external variable measures, had lacked attention (Legris et al. 2003). Based on a detailed analysis of 22 articles from six journals, Legris et al. (2003) found that only 60% of the TAM studies measured external variables and found no considerable pattern in regards to the external variables. The authors argued that it is important to study external variables because they are the ultimate drivers of usage. They also found that many studies examined intention-to-use systems rather than usage. As a result, the emphasis to provide empirical and theoretical evidence of the role of external variables and usage measures has been the main objective of our research. 2.3 Information system success model A multidimensional IS success model was created by DeLone and McLean (1992). The six interrelated dimensions of IS success were considered in the original model including organisation impact and individual impact, user satisfaction, IS use, and system and information quality. The results indicate that user satisfaction can be influenced by the information and system quality. The degree of IS use can influence the degree of user satisfaction directly, the individual’s performance indirectly, and eventually affect the whole organisation. After the publication of the first IS success model, some scholars suggested for more dimensions to be included in the model as the claim was that the IS success model was actually incomplete (Seddon and Kiew 1994; Seddon 1997). Therefore, Delone and McLean (2003) upgraded the IS success model (Fig. 2), and added service quality to the model. After that, several authors tried to test this model empirically (Gable et al. 2003; Sabherwal et al. 2006). However, Urbach et al. (2010) believed that it was necessary to include process quality and collaboration quality to the updated IS success model. The empirical result also supported the importance of these two variables in explaining the IS success (Urbach et al. 2010; Chen et al. 2013). In this research, the quality factors suggested by Urbach et al. (2010) are exceptions to the system quality
Fig. 2 The updated IS success model (Source: Delone and McLean 2003)
Educ Inf Technol
used to explain perceived usefulness. The system quality is not considered as a meaningful part in the concept which is close to the perceived ease of use that exists in the proposed model (Urbach et al. 2010; Urbach and Müller 2012).
3 Conceptual model and hypotheses development Figure 3 demonstrates the proposed framework for investigating the effects of the identified external variables of the perceived ease of use and perceived usefulness of the campus portal. Moreover, the effects of perceived usefulness and perceived ease of use on volume and frequency of campus portal usage have also been examined in this study. 3.1 Ease of finding and understanding Effortless locating and ease of navigation are the perceptions that are related to the ease of finding in a portal and website. Consistent graphics and terms and an understandable portal to use are related to ease of understanding. In addition, providing links to get more information in detail about the subject, and being visually readable and appealing can be other features (Brown 2002). Empirical support for the positive effect of ease of finding and ease of understanding on the perceived ease of use had been obtained from previous studies (Lederer et al. 2000; Brown 2002); therefore, the following hypotheses have been developed: H1: Ease of finding has a positive influence on the perceived ease of use of a campus portal.
Fig. 3 Proposed research framework
Educ Inf Technol
H2: Ease of understanding has a positive influence on the perceived ease of use of a campus portal.
3.2 Information quality Information quality refers to the quality of information which a system produces (Delone and McLean 2003). Information quality focuses on the quality of a campus portal’s output (i.e., the quality of the information that the portal provides) and its usefulness for the user. Usefulness will be influenced by information quality, which the IS success model posits (Delone and McLean 2003, 2004). The effect of information quality on perceived usefulness has been shown in studies on staff portals in general (Urbach et al. 2010). The information content provided in the campus portal should address, immensely, staff concerns regarding usefulness and reliability of information. Therefore, users’ perceptions of information quality should be able to influence their perceptions of the portal’s usefulness (Delone and McLean 2003, 2004) because they were able to acquire information at their desired level of standards after using the portal (Cao et al. 2005; Field et al. 2004). Thus the following hypothesis was developed: H3: Information quality has a positive influence on the perceived usefulness of a campus portal.
3.3 Process quality Process quality measures the quality of a campus portal’s support of an academic institution’s processes, such as grant approval, student-teacher evaluation, and updating teaching material. Accuracy, reliability, and efficiency are the terms which measure the quality of process support (Martini et al. 2009; Puschmann and Alt 2005). Campus portals are employed to both information exchange and business processes for electronical support (Martini et al. 2009). Accordingly, besides the established success factors, campus portal success is additionally determined by the quality with which the employee portal supports an academic institution’s processes. As a result, it can be stated that the demonstration of information quality at a high level is not necessarily an adequate and efficient support for processes in an employee portal. Thus, the following hypothesis was developed: H4: Process quality has a positive influence on the perceived usefulness of a campus portal.
3.4 Collaboration quality For efficient and even better collaboration efforts, the portals have paved the way (Ruivo et al. 2012; Urbach et al. 2010; Chen et al. 2007; and Johnson and Whang 2002). Collaboration quality covers the quality of a staff portal’s support of collaboration between its users. It enhances communication and improves the effectiveness and
Educ Inf Technol
efficiency of information sharing as well as of social networking (Benbya et al. 2004; Detlor 2000). Therefore, aside from the usual portal dimensions posited by the IS success model, another important question is how campus portals can facilitate better collaboration between staff members. The actual use of such collaborative systems can enhance overall performance (Easley et al. 2003). As such, the better the users’ perception of collaboration quality is, the more they will find the portal to be useful, and the more they will use the portal (Urbach et al. 2010). Therefore, the following hypothesis was developed: H5: Collaboration quality has a positive influence on the perceived usefulness of a campus portal.
3.5 Service quality Service quality refers to the support delivered by the service provider (Field et al. 2004; Delone and McLean 2003), which means good service quality covers aspects of the competence of the responsible service personnel, empathy, reliability, and responsiveness (Pitt et al. 1995) that directly influences usefulness (Cao et al. 2005; Harris and Goode 2004). Academic institutions’ staff perceptions of service quality, through the specific standards of good service, should be able to influence their evaluations of the campus portal’s usefulness (Delone and McLean 2003, 2004) because they experience an improved level of service (Cao et al. 2005; Liu and Arnett 2000) when using the campus portal which has been developed in the following hypothesis: H6: Service quality has a positive influence on the perceived usefulness of a campus portal.
3.6 Perceived ease of use Predominantly, a portal is perceived to be useful as it is easy and many previous studies have shown strong empirical support towards a positive relationship with perceived ease of use and perceived usefulness (King and He 2006; Zailani et al. 2016; Gilani et al. 2017). Hung et al. (2005) studied 39 articles on the TAM and found that 30 articles claimed that the perceived ease of use amongst users has affected, positively, perceived usefulness. Therefore, it is expected that the perceived ease of use of a campus portal affects its perceived usefulness and the extent of the usage of the portal. As such, the following hypotheses have been developed: H7: Perceived ease of use has a positive influence on the perceived usefulness of a campus portal. H8: Perceived ease of use has a positive influence on the usage volume of a campus portal. H9: Perceived ease of use has a positive influence on the usage frequency of a campus portal.
Educ Inf Technol
3.7 Perceived usefulness Previous studies have suggested that PU is an important determinant of users’ intention towards using a portal (Lee and Kim 2009; Lai 2001). When users believe that using a staff portal can enhance their productivity, they may want to contribute to using it (Lee and Kim 2009). Thus, the perceived usefulness of a campus portal affects the extent of the usage of the portal and the following hypotheses have been developed: H10: Perceived usefulness has a positive influence on the usage volume of a campus portal. H11: Perceived usefulness has a positive influence on the usage frequency of a campus portal.
4 Research methodology 4.1 Measure of constructs A survey has been employed comprising the profile of the respondents, external variables, perception of staff towards the campus portal (perceived usefulness and perceived ease of use), and usage of the campus portal (usage volume and usage frequency) in this study. To ensure content validity, the survey items were derived from those used in previous studies. The items of ease of finding and ease of understanding were adapted from Brown (2002) and Lederer et al. (2000). The information quality, process quality, collaboration quality, and service quality were derived from Urbach et al. (2010). The items of perceived ease of use, perceived usefulness, usage volume, and usage frequency were adapted from Burton-Jones and Hubona (2006). All constructs used within this study were measured using a five-point Likert-scale (1 = strongly disagree - 5 = strongly agree), except for usage frequency and usage volume. One fivepoint Likert scale (1 = don’t use at all - 5 = use several times a day) was used to measure usage frequency. Finally, the respondents were asked to write how many hours per week they would normally spend using the campus portal (Burton-Jones and Hubona 2006). 4.2 Procedures and collection of data The sampling frame of this study consisted of all the academic and non-academic staff of the University of Malaya (UM) in Malaysia. The data were collected from the academic and non-academic staff as both of them were the users of the campus portal in UM and thus, had knowledge and experience with the portal. UM had 2807 academic staff and 3030 non-academic staff at the time of the data collection. The sampling list was obtained from the website of UM. An online end-user survey was provided and its link was sent to all UM staff using UM info. To increase the response rate, the invitation emails were sent twice, once for the initial invitation and once as a reminder so that the participants could respond to the online questionnaire by entering the URL provided during the tenure of the one month study being conducted. Out of 5883 invitation emails sent to the working emails of the staff, 341 usable data were collected, resulting in an effective response rate of 5.8%.
Educ Inf Technol
In ensuring that the received responses were representative of the sample firms, a non-response bias was conducted following the procedure suggested by Armstrong and Overton (1977). The responses which were received before sending the first reminder were defined as early responses (two weeks from the first mailing); whereas, those received after the expected date were referred to as late responses. According to this criterion, 154 responses were considered as early responses and 187 responses were considered as late responses. The Chi-square test was used to test the significant difference between the characteristics of the early and late respondents. At a 5% significance level, no differences were detected between the Bearly^ and Blate^ respondents, by which offering that the non-response bias was not a setback with regards to the data collected in this study. 4.3 Analysis To test the research model, the current research employed the partial least squares (PLS) technique of structural equation modelling using the SmartPLS Version 3.0. The reason to select this technique was due to the fact of its capacity to analyse a complicated model (Hair et al. 2011). In this research, to analyse the data, a two-step approach for data analysis was applied on the recommendation of Hair et al. (2013). In the first step, the measurement model was analysed and then, the relationships between the structures of the underlying constructs were assessed (see Nikbin et al. 2015; Yusof et al. 2016; Zailani et al. 2015). Before the dementing relationship within the model, the validity and reliability of the measures were determined by employing this method in the current research.
5 Results 5.1 Profile of the respondents The final sample consisted of 129 (37.8%) males and 212 (62.2%) females of which the 129 (37.8%) respondents were between the ages of 31 and 40 years old, followed by 90 (26.4%) of the respondents being between the age group of 41–50 years old, 64 (18.8%) of the respondents were between the ages of 51 and 60 years old, 47 (13.8%) of the respondents were less than 30 years old, and only 11 (3.2%) of the respondents fell into the age group of above 61 years old, respectively. Around 41.3% of the respondents involved consisted of academic staff and 58.7% of them were nonacademic staff; and most importantly, all the respondents involved in this study had experience using the UM campus portal. 5.2 Measurement model results The reflective constructs were examined in terms of reliability and validity (see Nikbin et al. 2014; Soltanian et al. 2016; Iranmanesh et al. 2017). Composite reliability (CR) is equivalent to Cronbach’s alpha and is measured in relation to internal reliability. The CR of all the constructs, which is shown in Table 1, indicates that the CR of all the constructs was above 0.7, which satisfies the rule of thumb in Hair et al. (2013). Hair et al. (2010) recommended the acceptance of items with a minimum loading of 0.7. The reliability of
Educ Inf Technol Table 1 Measurement model evaluation Constructs
Items
Factor CR loadings
Ease of finding (EF)
The Information I need is easy to find within the Campus Portal.
0.876
Ease of understanding (EU)
Information quality (IQ)
Process quality (PQ)
Collaboration quality (CQ)
The Campus Portal is easy to navigate.
0.857
The Campus Portal provides quick links to the information that I need.
0.885
The Campus Portal provides quick links to the task that I need to accomplish.
0.883
The Campus Portal uses consistent terms.
0.789
The Campus Portal uses consistent graphics.
0.706
The Campus Portal uses understandable terms.
0.851
The Campus Portal pages are visually understandable.
0.797
The Campus Portal pages are easy to read.
0.767
The information provided by the Campus Portal is useful.
0.795
The information provided by the Campus Portal is interesting.
0.879
The information provided by the Campus Portal is reliable.
0.853
The information provided by the Campus Portal is complete.
0.878
The information provided by the Campus Portal is up-to-date.
0.835
The Campus Portal supports the work process efficiently.
0.939
The Campus Portal supports the work process reliably.
0.854
The Campus Portal supports the work process accurately.
0.860
The Campus Portal supports the easy initiation of work processes.
0.938
The Campus Portal supports the work process in a way that allows one to understand it.
0.750
The Campus Portal supports the work process fully.
0.823
The Campus Portal enables easy and comfortable communication with my colleagues.
0.914
The Campus Portal supports an effective and efficient 0.826 sharing of information with my colleagues. The Campus Portal enables a comfortable storing and 0.926 sharing of documents with my colleagues. The Campus Portal allows me to easily and quickly locate my colleagues’ contact information.
0.732
The Campus Portal allows me to enter my 0.813 competence profile easily and in a structured way. The Campus Portal enables me to identify experts within my university easily and quickly.
0.825
AVE
0.929 0.766
0.888 0.614
0.928 0.720
0.946 0.745
0.942 0.701
Educ Inf Technol Table 1 (continued) Constructs
Items
Factor CR loadings
AVE
The Campus Portal supports an effective networking 0.811 between the campuses of my university. Service quality (SQ)
The help desk of the Campus Portal is always willing 0.945 to help whenever I need support with the portal. The help desk of the Campus Portal provides personal attention when I experience problems with the portal.
0.930 0.770
0.843
The help desk of the Campus Portal provides services 0.781 related to the portal at the promised time. The help desk of the Campus Portal has sufficient knowledge to answer my questions in respect to the portal. Perceived ease of use (PEU) The Campus Portal is easy to use.
Perceived usefulness (PU)
0.930
0.876
The Campus Portal is easy to learn.
0.883
The Campus Portal is user friendly.
0.906
The Campus portal is easy to master.
0.853
Using the Campus Portal in my job helps me to accomplish tasks more quickly.
0.890
Using the Campus Portal improves my job performance.
0.940
Using the Campus Portal in my job increases my productivity.
0.895
0.932 0.774
0.943 0.768
Using the Campus Portal enhances the effectiveness 0.847 of my job. Overall, I find the Campus Portal to be useful for my job.
0.805
CR Composite Reliability, AVE Average Variance Extracted
the individual items was reasonably judged, given that all scales reported loadings that exceeded 0.7. To evaluate convergent validity, the Average variance extracted (AVE) was utilised and the value of the AVE should have exceeded 0.5. The results illustrated that the convergent validity of these constructs was acceptable (Fornell and Larcker 1981). To evaluate the discriminant validity in the constructs, two techniques were used (see Fathi et al. 2016; Zailani et al. 2016; Zainuddin et al. 2017). Firstly, the indicator cross-loadings were investigated to examine whether each opposing construct did not exceed any indicator load (Hair et al. 2012). Secondly, the value of the intercorrelations between the model constructs should have been surpassed by the square root of the AVE of a single construct (Table 2). Both analyses confirmed the discriminant validity of all the constructs. 5.3 Structural model assessment The measurement model had satisfactory results. Thereafter, the structural model was assessed. The accuracy of the predictions made using this model was determined
Educ Inf Technol Table 2 Discriminant validity coefficients 1
2
3
4
5
6
Ease of finding
0.875
Ease of Understanding
0.586
0.783
Information quality
0.671
0.632
0.849
Process quality
0.636
0.465
0.680
0.863
Collaboration quality
0.684
0.525
0.652
0.804
Service quality
0.602
0.595
0.604
0.671
0.627
0.877
Perceived ease of use
0.602
0.751
0.554
0.474
0.475
0.670
7
8
9
10
0.837 0.880
Perceived usefulness
0.685
0.464
0.673
0.654
0.641
0.443
0.455
0.875
Usage frequency
0.093
0.081
0.093
0.080
0.065
0.229
0.178
0.105
1.000
Usage volume
0.057
0.047
0.044
0.160
0.008
0.151
0.101
0.113
0.409
1.000
Diagonal elements represent the square root of the average variance extracted (AVE)
through the explained variance portion. The model was able to account for 66.9%, 64.4%, 4.2%, and 7.5% of the variances in the perceived ease of use, perceived usefulness, usage volume, and usage frequency, respectively. In addition to estimating the R2 magnitude, the predictive relevance evaluation measure developed by Stone (1974) and Geisser (1975) was incorporated as another tool to determine model fit. According to a blindfolding process in PLS, the measurement of the predictive relevance could be computed through the calculation of the value of Stone–Geisser Q2 (cross-validated redundancy). If the value of Q2 exceeds zero, the model would exhibit predictive relevance as reported by Chin (2010). In this research, the value acquired was 0.256, which was significantly greater than zero, for the average cross-validated redundancy. Therefore, a high predictive relevance and acceptable fit were exhibited by the model. Non-parametric bootstrapping was applied (Wetzels et al. 2009) with 2000 replications to test the structural model, which is illustrated in Table 3 and has resulted from the PLS analysis. The results indicate that the effects of ease of finding (β = 0.399, p < 0.001) and ease of understanding (β = 0.518, p < 0.001) on perceived ease of use are significant and positive. Therefore, H1 and H2 are supported. As shown in Table 3, information quality (β = 0.628, p < 0.001), process quality (β = 0.320, p < 0.001), and service quality (β = 0.219, p < 0.01) have significant effects on perceived usefulness. Meanwhile, collaboration quality (β = 0.001, p > 0.05) and perceived ease of use (β = 0.102, p > 0.05) have no effect. Thus, H3, H4, and H6 are supported; whilst H5 and H7 are not supported. The results show that perceived ease of use has a significant effect on both usage volume (β = 0.193, p < 0.05) and usage frequency (β = 0.285, p < 0.01). Furthermore, perceived usefulness also has a significant effect on both usage volume (β = 0.200, p < 0.05) and usage frequency (β = 0.235, p < 0.01). Hence, H8 to H11 are supported. The strength of the effect of the determinants on the perceived ease of use, perceived usefulness, usage volume, and usage frequency were examined by the effect size (f2) (Hair et al. 2013). Ease of understanding had a higher impact on the perceived ease of use (f2 = 0.533) compared to the ease of finding (f2 = 0.316). Information quality had the highest effect on the perceived usefulness (f2 = 0.411), followed by the procedural
Educ Inf Technol Table 3 Path coefficient and hypothesis testing Hypothesis
Relationship
Path coefficient
Effect size
Decision
H1
EF - > PEU
0.399***
0.316
Supported
H2
EU - > PEU
0.518***
0.533
Supported
H3
IQ - > PU
0.628***
0.411
Supported
H4
PQ - > PU
0.320***
0.086
Supported
H5
CQ - > PU
0.001
0.000
Not Supported
H6
SQ - > PU
0.219**
0.052
Supported
H7
PEU - > PU
0.102
0.015
Not Supported
H8
PEU - > UV
0.193*
0.031
Supported
H9
PEU - > UF
0.285**
0.069
Supported
H10
PU - > UV
0.200*
0.033
Supported
H11
PU - > UF
0.235**
0.047
Supported
T values were computed through the bootstrapping procedure with 341 cases and 2000 samples UV Usage Volume, UF Usage Frequency *p < 0.05, **p < 0.01, ***p < 0.001 (one tail)
quality (f2 = 0.086) and service quality (f2 = 0.052). In addition, although the effect of the perceived ease of use on the usage frequency was higher than the perceived usefulness, their effects on the usage volume were vice versa.
6 Discussions The results of the study show that ease of finding and ease of understanding have significant effects on perceived ease of use which is consistent with the findings of Lederer et al. (2000) and Brown (2002). This implies that the ease with which information can be understand and found in web portals are important in shaping the staff’s perception of ease of use. As such, the information should be easy to navigate and quick links are needed to the common information that staff members are mostly looking for and the tasks that they need to do. For example, a quick link for access to emails and clock in systems is needed. Furthermore, understandable terms and consistent terms and graphics should be used in a campus portal to make it understandable. For example, using the term Bclock in/ out^ is more appropriate than BWeb Diary^ for the quick link to access the clock in system. The results also show that information quality has a positive effect on perceived usefulness. This result is in line with the finding of Delone and Mclean (2004) and Urbach et al. (2010). Practically speaking, university staff search through campus portals to look for the correct and useful information that will suit their needs and help them to complete their assigned tasks. Amongst the four quality factors, information quality has the highest impact on perceived usefulness (f2 = 0.411). Thus, the impact on usefulness has been obtained when useful, interesting, reliable, and up-to-date information is provided. Our results confirm that process quality has a significant effect on perceived usefulness. This relationship is supported in the Chen et al. (2013) study. It suggests
Educ Inf Technol
that the campus portal should support the work process efficiently and accurately. As such, the campus portal needs to be designed in a way that facilitates the understanding of the work process. The results also show a positive relationship between service quality and perceived usefulness. The relationship is strongly supported in the literature (Cao et al. 2005; Harris and Goode 2004; and Delone and McLean 2003, 2004). This implies that the quality of the help desk is an important factor for university staff to find the web portal as a useful tool. The help desk of the portal should be responsible and knowledgeable. Our results indicate that the collaboration quality has no significant effect on perceived usefulness which is not consistent with the finding of Chen et al. (2013). Considering the significant impacts of information quality and process quality, and an insignificant effect of collaboration quality, it can be concluded that the staff want to use campus portals for finding information and doing tasks, and not collaboration. The findings indicate that both perceived ease of use and perceived usefulness have a significant effect on usage volume and usage frequency of campus portal. The impact of perceived ease of use on perceived usefulness is also supported. The significant effect of both the perceived ease of use and perceived usefulness on the usage volume and usage frequency of the campus portal indicates that the staff should find the campus portal easy to find and useful to use it frequently, and spend more time in the portal. As such, the portal designer should give special notice to the antecedent of the perceived portal ease of use and usefulness. However, the low R2 value of usage volume and usage frequency indicates that there are many more factors that have an effect on usage volume and usage frequency of campus portals, which need to be investigated in future studies.
7 Theoretical and practical implications The findings from this study have important implications for researchers and practitioners who promote campus portals in their academic institutions. From the aspect of the theoretical contribution, the study would be the first to consider the determinants of the perceived ease of use and perceived usefulness in campus portals and their impact on the usage frequency and usage volume. This study contributes to a theoretical understanding of the factors that promote campus portal usage, perception of ease of use, and perception of usefulness. Given the growth and strategic importance of campus portals in academic institutions, the factors that improve the ease of use, usefulness, and usage of campus portals should be examined. Furthermore, this study integrates the TAM and the IS success model to justify and extend the TAM to the campus portal. From a practical point of view, our model proposes a way to predict and evaluate the success of the campus portals in academic institutions. In accordance with our results, practitioners now know more about the levers that help to improve their campus portals and can prioritise their investments accordingly. Our results indicate that ease of finding and ease of understanding have to be considered when aiming for developing an easy to use campus portal. Furthermore, information, process, and service quality should be established to promote the perception of usefulness.
Educ Inf Technol
8 Limitations and future studies Even though the study had accomplished its objective, its limitations should be considered before generalising the results. Firstly, the survey was conducted in the University of Malaya in Malaysia which raises concerns about the generalisability of the findings to other universities and countries. The future studies can test the proposed model of this industry in other universities and countries. Secondly, the study conducted had used a short-term snapshot of the campus portal users’ behaviour. Additional research efforts with longitudinal studies would provide a clearer picture of how the users and the relationships amongst the variables change over time. In addition, as campus portals become a means of task processing and information searching, it will be useful to examine the impacts of campus portal usage on staff performance.
References Ainin, S., Bahri, S., & Ahmad, A. (2012). Evaluating portal performance: A study of the National Higher Education Fund Corporation (PTPTN) portal. Telematics and Informatics, 29(3), 314–323. Al-Busaidi, K. A. (2010). Revealing the strengths and weakness of a corporate portal. Communication of the IBIMA. Al-Busaidi, K. A. (2012). The payoff of corporate portal usage in an academic institution. Campus-Wide Information Systems, 29(5), 368–379. Aneja, A., Rowan, C., & Brooksby, B. (2000). Corporate portal framework for transforming content chaos on intranets. Intel Technology Journal, 4(1), 1–7. Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14(3), 396–402. Bakar, A. A., Razak, F. Z. A., & Abdullah, W. S. W. (2014). Measuring employee portal success: An evidence from Malaysian private higher institution. Middle-East Journal of Scientific Research, 21(1), 201–208. Benbya, H., Passiante, G., & Belbaly, N. A. (2004). Corporate portal: A tool for knowledge management synchronization. International Journal of Information Management, 24(3), 201–220. Bhuasiri, W., Xaymoungkhoun, O., Zo, H., Rho, J. J., & Ciganek, A. P. (2012). Critical success factors for elearning in developing countries: A comparative analysis between ICT experts and faculty. Computers & Education, 58(2), 843–855. Bringula, R. P. (2013). Influence of faculty-and web portal design-related factors on web portal usability: A hierarchical regression analysis. Computers & Education, 68, 187–198. Bringula, R. P., & Basa, R. S. (2011). Factors affecting faculty web portal usability. Educational Technology & Society, 14(4), 253–265. Brown, I. T. (2002). Individual and technological factors affecting perceived ease of use of web-based learning technologies in a developing country. The Electronic Journal of Information Systems in Developing Countries, 9(5), 1–15. Burton-Jones, A., & Hubona, G. S. (2006). The mediation of external variables in the technology acceptance model. Information Management, 43(6), 706–717. Cao, M., Zhang, Q., & Seydel, J. (2005). B2C e-commerce web site quality: An empirical examination. Industrial Management & Data Systems, 105(5), 645–661. Chan, E. H., & Liu, C. (2007). Corporate portals as extranet support for the construction industry in Hong Kong and nearby regions of China. ITCon, 12, 181–192. Chen, M., Zhang, D., & Zhou, L. (2007). Empowering collaborative commerce with web services enabled business process management systems. Decision Support Systems, 43(2), 530–546. Chen, J. V., Chen, Y., & Capistrano, P. S. (2013). Process quality and collaboration quality on B2B ecommerce. Industrial Management & Data Systems, 113(6), 908–926. Chin, W. W. (2010). How to write up and report PLS analyses. In V. E. Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications in marketing and related fields (pp. 655–690). Berlin: Springer.
Educ Inf Technol Daniel, E. M., & Ward, J. M. (2005). Enterprise portals: Addressing the organisational and individual perspectives of information systems. In: Proceedings of the 13th European conference on information systems (ECIS 05), May 26–28, Regensburg, Germany. Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: theory and results. Doctoral Dissertation, Sloan School of Management, Massachusetts Institute of Technology. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319–340. DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60–95. Delone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30. Delone, W. H., & Mclean, E. R. (2004). Measuring e-commerce success: Applying the DeLone & McLean information systems success model. International Journal of Electronic Commerce, 9(1), 31–47. Detlor, B. (2000). The corporate portal as information infrastructure: Towards a framework for portal design. International Journal of Information Management, 20(2), 91–101. Easley, R. F., Devaraj, S., & Crant, J. M. (2003). Relating collaborative technology use to teamwork quality and performance: An empirical analysis. Journal of Management Information Systems, 19(4), 247–265. Eckerson, W. W. (1999). Plumtree blossoms – New version fulfills Enterprise portal requirements. Boston: Patricia Seybold Group. Fathi, E., Zailani, S., Iranmanesh, M., & Kanapathy, K. (2016). Drivers of consumers’ willingness to pay for halal logistics. British Food Journal, 118(2), 464–479. Field, J. M., Heim, G. R., & Sinha, K. K. (2004). Managing quality in the e-service system: Development and application of a process model. Production and Operations Management, 13(4), 291–306. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. Gable, G., Sedera, D., & Chan, T. (2003). Enterprise systems success: A measurement model. In: Proceedings of the 24th international conference on information systems (ICIS 03), December 14–17, Seattle, Washington. Geisser, S. (1975). The predictive sample reuse method with applications. Journal of the American Statistical Association, 70(350), 320–328. Gilani, M. S., Iranmanesh, M., Nikbin, D., & Zailani, S. (2017). EMR continuance usage intention of healthcare professionals. Informatics for Health & Social Care, 42, 153–165. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis (7th ed.). Upper Saddle River: Prentice Hall. Hair, J. F., Ringle, C. M., & Sarsedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–151. Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414–433. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2013). A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks: Sage. Harris, L. C., & Goode, M. M. (2004). The four levels of loyalty and the pivotal role of trust: A study of online service dynamics. Journal of Retailing, 80(2), 139–158. Hung, S. Y., Liang, T. P., & Chang, C. M. (2005). A meta-analysis of empirical research using TAM. Journal of Information Management,12(4), 211-234. Iranmanesh, M., Zailani, S., Moeinzadeh, S., & Nikbin, D. (2017). Effect of green innovation on job satisfaction of electronic and electrical manufacturers’ employees through job intensity: Personal innovativeness as moderator. Review of Managerial Science, 11(2), 299–313. Johnson, M., & Whang, S. (2002). E-business and supply chain management: An overview and framework. Production and Operations Management, 11(4), 413–423. King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information Management, 43(6), 740–755. Lai, V. S. (2001). Intraorganizational communication with intranets. Communications of the ACM, 44(7), 95–100. Lederer, A. L., Maupin, D. J., Sena, M. P., & Zhuang, Y. (2000). The technology acceptance model and the world wide web. Decision Support Systems, 29(3), 269–282. Lee, S., & Kim, B. G. (2009). Factors affecting the usage of intranet: A confirmatory study. Computers in Human Behavior, 25(1), 191–201. Lee, H. S., Choi, Y. H., & Jo, N. O. (2009). Determinants affecting user satisfaction with campus portal services in Korea. Journal of Internet Banking and Commerce, 14(1), 1–18.
Educ Inf Technol Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information Management, 40(3), 191–204. Lin, J. C. C., & Lu, H. (2000). Towards an understanding of the behavioural intention to use a web site. International Journal of Information Management, 20(3), 197–208. Liu, C., & Arnett, K. P. (2000). Exploring the factors associated with web site success in the context of electronic commerce. Information Management, 38(1), 23–33. Manouselis, N., Kastrantas, K., Sanchez-Alonzo, S., Caceres, J., Ebner, H., Palmer, M., & Naeve, A. (2009). Architecture of the organic. Edunet web portal. International Journal of Web Portals, 1(1), 71–91. Martini, A., Corso, M., & Pellegrini, L. (2009). An empirical roadmap for intranet evolution. International Journal of Information Management, 29(4), 295–308. Masrek, M. (2007). Measuring campus portal effectiveness and the contributing factors. Campus-Wide Information Systems, 24(5), 342–354. Mphidi, H., & Snyman, R. (2004). The utilisation of an intranet as a knowledge management tool in academic libraries. The Electronic Library, 22(5), 393–400. Nikbin, D., Hyun, S. S., Iranmanesh, M., & Foroughi, B. (2014). Effects of perceived justice for coaches on athletes' trust, commitment, and perceived performance: A study of futsal and volleyball players. International Journal of Sports Science and Coaching, 9(4), 561–578. Nikbin, D., Iranmanesh, M., Hyun, S. S., Baharun, R., & Kim, I. (2015). The role of airline travelers’ prerecovery emotions during the service recovery process. Journal of Travel & Tourism Marketing, 32(6), 677–691. Pitt, L. F., Watson, R. T., & Kavan, C. B. (1995). Service quality: A measure of information systems effectiveness. MIS Quarterly, 19(2), 173–187. Puschmann, T., & Alt, R. (2005). Developing an integration architecture for process portals. European Journal of Information Systems, 14(2), 121–134. Ruivo, P., Oliveira, T., & Neto, M. (2012). ERP use and value: Portuguese and Spanish SMEs. Industrial Management & Data Systems, 112(7), 1008–1025. Sabherwal, R., Jeyaraj, A., & Chowa, C. (2006). Information system success: individual and organizational determinants. Management Science, 52(12), 1849–1864. Seddon, P. B. (1997). A respecification and extension of the DeLone and McLean model of IS success. Information Systems Research, 8(3), 240–253. Seddon, P. B., & Kiew, M.-Y. (1994). A partial test and development of the DeLone and McLean model of IS success. In: Proceedings of the 15th International Conference on Information Systems (ICIS 94), Vancouver, 14–17 December. Shilakes, C. C., & Tylman, J. (1998). Enterprise information portal – Industry overview. New York City: Merrill Lynch, Inc.. Soltanian, M., Zailani, S., Iranmanesh, M., & Aziz, A. A. (2016). Motivations of SME entrepreneurs to become halalpreneurs. Journal of Science and Technology Policy Management, 7(2), 173–189. Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, 36(2), 111–133. Tojib, D.R., Sugianto, L.-F., & Sendjaya, S. (2006). A conceptual model for B2E portal user satisfaction. In: Proceedings of the International Conference on Business and Information, Singapore, 12–14 July. Urbach, N., & Müller, B. (2012). The updated DeLone and McLean model of information systems success. In Information systems theory (pp. 1–18). New York: Springer. Urbach, N., Smolnik, S., & Riempp, G. (2010). An empirical investigation of employee portal success. The Journal of Strategic Information Systems, 19(3), 184–206. Wetzels, M., Odekerken-Schroder, G., & van Oppen, C. (2009). Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS Quarterly, 33(1), 177–195. Wu, J., & Du, H. (2012). Toward a better understanding of behavioral intention and system usage constructs. European Journal of Information Systems, 21, 680–698. Yusof, N. A., Abidin, N. Z., Zailani, S. H. M., Govindan, K., & Iranmanesh, M. (2016). Linking the environmental practice of construction firms and the environmental behaviour of practitioners in construction projects. Journal of Cleaner Production, 121, 64–71. Zailani, S. H., Seva Subaramaniam, K., Iranmanesh, M., & Shaharudin, M. R. (2015). The impact of supply chain security practices on security operational performance among logistics service providers in an emerging economy: Security culture as moderator. International Journal of Physical Distribution and Logistics Management, 45(7), 652–673.
Educ Inf Technol Zailani, S., Iranmanesh, M., Masron, T. A., & Chan, T. H. (2016). Is the intention to use public transport for different travel purposes determined by different factors? Transportation Research Part D: Transport and Environment, 49, 18–24. Zainuddin, Z. B., Zailani, S., Govindan, K., Iranmanesh, M., & Amran, A. (2017). Determinants and outcome of a clean development mechanism in Malaysia. Journal of Cleaner Production, 142, 1979–1986. Zhang, L., Zhu, J., & Liu, Q. (2012). A meta-analysis of mobile commerce adoption and the moderating effect of culture. Computers in Human Behavior, 28, 1902–1911.
