Behavioral intention to use electronic resources by distance learners: An extension of the technology acceptance model

Abstract

This study aimed to discover the factors affecting behavioral intention to use electronic resources by distance learners of the Open University of Sri Lanka, and to develop a model explaining behavioral intention to use such resources. Using the Technology Acceptance Model (TAM) as the theoretical basis, this study explored the effect of five external factors on the behavioral intention of distance learners toward using e-resources. A quantitative research approach was used, and data were collected from a survey of 379 active undergraduates of the Open University of Sri Lanka. Partial Least Squares-Structural Equation Modeling (PLS-SEM) was conducted to obtain the results of the study. The results of PLS-SEM reveal that the distance mode of learning and relevance of information are the two major external factors that affect behavioral intention to use e-resources. Computer self-efficacy and user satisfaction also have a significant influence on the dependent variable. Social influence, on the other hand, shows no significant impact. Of the TAM constructs, perceived usefulness, perceived ease of use, and user attitudes significantly affect e-resource utilization behavior.

Keywords

Behavioral intention distance learners e-resources structural equation modeling technology acceptance model

Introduction

In this technological era, e-resources have influenced the educational sector of the world, resulting in both benefits and drawbacks. This trend has also significantly impacted libraries and information professionals, where information professionals have an opportunity to bring knowledge together to meet the information needs of different kinds of information seekers. Hence, advancements in Information and Communication Technology (ICT) affect library operations primarily by changing the delivery format of information resources and services. According to Daramola (2016), the e-library has transformed how information is accessed and utilized because it saves time, since people can access publications at their convenience.

Distance learning is a popular method of education with advantages for both students and institutions. It is an effective and economical way of extending educational opportunities, thereby contributing to human resource development (Boadi and Letsolo, 2004). It allows educational institutions to expand their student populations even with limited resources, without investing more money on physical facilities (Tury et al., 2015). It also enables students to access learning from a distance, without such a process intruding on their careers and other living conditions.

The vast and increasing availability of e-resources, the introduction of virtual learning classrooms, and the adoption of online learning management systems by many universities and higher educational institutes show the growing use of ICT in teaching and learning. Katz (2002) stated that “ICT use in education, including distance learning, can be described as a major advancement for learning and instruction.” There is a growing body of literature that recognizes the increasing use of and preference for e-resources by distance learners in their academic activities (Byrne and Bates, 2009; Kelley and Orr, 2003; Liu and Yang, 2004; Parsons, 2010; Tang and Tseng, 2013; Tury et al., 2015). Therefore, the use of e-resources in distance education plays a vital role in fulfilling the information needs of learners.

Parallel to the growing trend toward using e-resources in distance education, investigating the factors affecting behavioral intention to use e-resources would be crucial in actively and productively developing interventions in terms of expanding digital library collections, conducting training and workshops to increase awareness among learners, and integrating e-resources with online learning management systems. Further, study findings would support setting up national-level plans to achieve the optimum use of ICT in teaching and learning. This supports ensuring the smooth running of the teaching-learning process while coping with unforeseen and unexpected changes happening in the world, parallel to the advancement in technology. The study also contributes significantly to the knowledge base in information behavior, e-information resources, technology acceptance, distance learning, and distance learner behavior.

According to the literature, only a few research studies have been carried out addressing the use of e-resources by distance learners of Sri Lanka, and these have been largely descriptive in nature (Gunasekera, 2010, 2012, 2014; Karunarathna, 2015). Karunarathna (2015) carried out a study to identify the use of e-resources by students following law degrees at the Anuradhapura Regional Centre of the OUSL, and came up with findings on the use of e-resources, frequency of use, places of accessing e-resources, purposes of using e-resources, types of e-resources and barriers faced in accessing e-resources. Gunasekera (2014) carried out a similar study to investigate the use of e-resources, the main reasons for using the computer laboratory by distance learners attached to the Matara Regional Centre of the OUSL, and the problems faced in using e-resources. However, such attempts have not focused on the factors affecting behavioral intention to use e-resources by distance learners. This implies the need for an extensive and advanced research study on distance leaner’s e- information behavior. Moreover, the literature found no evidence for the application of the Technology Acceptance Model (TAM) and the identification of the determinants of behavioral intention to use e-resources by distance learners in the Sri Lankan context.

With this aim in mind, the current study attempts to discover the factors affecting behavioral intention to use e-resources by distance learners of the OUSL using the Technology Acceptance Model, and to develop a model explaining the behavioral intention of distance learners’ toward using e-resources. The study focused on distance learners attached to the OUSL, the premier open and distance education institution of Sri Lanka that emphasizes student-centered learning. The activities of the University are underpinned by the Open Distance Learning (ODL) philosophy aiming to expand opportunities for higher education to students, regardless of age, previous qualifications, income, geographic, and employment barriers (The Open Unievrsity of Sri Lanka, 2019).

The study extended the TAM to investigate the factors affecting behavioral intention to use e-resources by distance learners of the OUSL. TAM was introduced by Davis (1986) as an adaptation of the Theory of Reasoned Action, and was specifically developed for modeling user acceptance of information systems. Davis et al. (1989) mention that generally, TAM could be used to explain the determinants of computer/ technology acceptance and specifically to explain individual user behavior across a wide range of end-user computing technologies and user populations. Therefore, TAM is applicable in explaining the factors affecting a user’s behavioral intention toward a particular system. PLS-SEM, a key multivariate analysis technique, was employed in the study to examine the relationships between the constructs of the proposed model. The ability of PLS-SEM to simultaneously estimate a series of direct, indirect, and moderating effects of constructs has enabled many researchers to select PLS-SEM for their studies.

Literature review

Theory and concepts

The study applied TAM to explore the effect of external variables and TAM constructs on behavioral intention toward using e-resources by distance learners. TAM is a widely used model for comprehending users’ attitudes and intentions to integrate new technologies for different purposes.

The primary aim of TAM was to predict and explain the usage of information technology using two theoretical constructs: Perceived Usefulness (PU) and Perceived Ease of Use (PEU). Accordingly, PU and PEU were considered the fundamental determinants of system use (Davis, 1989). Further, TAM (Figure 1) aimed to provide a basis for modeling the impact of external factors on Internal Beliefs, Attitudes, and Behavioral Intentions (Davis et al., 1989).

Figure 1.

Technology acceptance model.

Davis et al. (1989) pointed out that in the TAM, computer usage is determined by behavioral intention (BI), while BI is jointly determined by the person’s attitude toward using the system and PU. BI is considered an immediate antecedent of usage behavior and indicates an individual’s readiness to perform a specific behavior. According to Figure 1, ATT is jointly determined by PU and PEU. TAM further theorized that PU and PEU mediate the effects of external variables on behavioral intention to use.

Extensive research studies have used the TAM to explain behavioral intention to use different information technology-related systems (Al-Aulamie, 2013; Dumpit and Fernandez, 2017; Farahat, 2012; Hanif et al., 2018; Izuagbe et al., 2016; Kim, 2005; Kripanont, 2007; Lee and Lehto, 2013; Shih, 2004; Tao, 2009; Tella, 2011). According to the literature, TAM has been applied to explain and predict user behavior in different areas, such as online learning, internet utilization behavior, social media usage, e-resources usage, user acceptance of YouTube, open education resources usage, and user acceptance of computer technology. These studies have found that various socio-cultural, economic, educational, and technical factors affecting the acceptance and use of different technological systems. The success of TAM in supporting studies to predict behavioral intention to use technology-integrated products in education encourages the establishment of this study’s proposed model. After an extensive literature review, the current study identified four (04) external factors; user satisfaction (US), relevance (R), social influence (SI), and computer/ internet self-efficacy (SE) in evaluating behavioral intention to use e-resources by distance learners. The authors selected the distance method of learning as another external factor in the study, assuming that the method of learning (ML) is a direct predictor of ease of use of e-resources.

Model formulation and related hypotheses

This study proposed a model to evaluate the factors affecting behavioral intention to use e-resources by distance learners based on the TAM developed by Davis (1989). The literature revealed that many research studies have been carried out using TAM to explain user behavior in accepting different kinds of technological products and systems. Accordingly, TAM can be applied to explain and understand students’ acceptance behavior of e-resources.

Based on the existing literature, the study proposed five (05) external variables, namely, relevance of information, computer/ internet self-efficacy, social influence, user satisfaction, and method of learning, which would affect behavioral intention to use e-resources by distance learners of OUSL. Figure 2 shows the proposed model that extends TAM by incorporating five external variables. In Figure 2, the relationships between TAM constructs are shown in solid lines, while the connections between external variables and TAM constructs are shown in dashed lines.

Figure 2.

Proposed model using TAM to identify factors affecting behavioral intention to use e-resources.

Accordingly, 11 hypotheses were developed as follows.

Relevance of information and perceived usefulness of e-resources

Relevance is defined as “the degree to which something is closely connected with the subject of concern or the situation one is thinking about” (Thong et al., 2002). Shih (2004) stated that relevance is a subjective measure of personal cognition related to information needs, and this may influence individual evaluations in looking for the requisite information and performance when using the retrieved information. Shih (2004) further expressed that people look for relevant information in order to match their current knowledge state with their ability to perform the task. Research studies have used relevance as a factor to measure information quality (Ahituv, 1980; Bailey and Pearson, 1983). DeLone and McLean (1992) have concluded that information quality is an important measure of the success of information systems from the user perspective. Relevance of information is a measure used in evaluating the utility of a particular information system. Under the assumption that the PU of e-resources will be higher if users find more relevant information from them, the study proposes the first hypothesis:

(H1): Relevance of information has a positive effect on the PU of e-resources.

Computer self-efficacy and perceived usefulness of e-resources

Many of the TAM-based research studies have used SE as an external factor (Hanif et al., 2018; Hong et al., 2002; Kripanont, 2007; Lee and Lehto, 2013; Tarhini et al., 2016; Tella, 2011). SE is defined as “one’s belief about his/her ability to accomplish a particular task using a computer” (Kher et al., 2013). It concerns individuals’ self-confidence about their abilities to perform a given task. Some studies have found SE as an essential determinant that directly and positively influences the user’s behavioral intention and actual usage of ICT-related systems (Tarhini et al., 2016; Tella, 2011). Lee and Lehto (2013) noted that PU was significantly predicted by SE. This reflects that SE has an influence on motivation to perform a task, as well as on the subsequent outcome expectations. In general, it is supposed that e-resources users with a higher level of SE are more likely to adopt e-resources than those with lower SE. Therefore, in line with previous research studies, the following hypothesis has been proposed:

(H2): SE has a positive effect on the PU of e-resources.

Social influence and perceived usefulness of e-resources

Subjective norms and social norms are some similar terms used for SI. SI is about a person’s belief that the majority of important individuals in their life think they should or should not perform the behavior in question (Ajzen, 1991). This expresses about the pressure coming from external society, which is closely related to an individual, affects the individual’s behavior of engaging in a specific action. A study done by Karahanna and Straub (1999), on the critical factors of electronic library acceptance found that SI is a critical component that affects students’ acceptance and use of the electronic library. Hanif et al. (2018) also demonstrated a significant positive influence of subjective norms on the PU of an e-learning system. Individuals may perceive e-resources as a useful format of information due to the opinions and beliefs of those close to them and those who are influential in their lives. Building on this, the study hypothesizes that:

(H3): SI has a positive effect on the PU of e-resources.

User satisfaction and perceived usefulness of e-resources, user satisfaction and behavioral intention to use e-resources

Prior researches have indicated that user satisfaction is an important factor affecting the success of electronic systems. This is the extent to which users are satisfied and pleased with their prior use of an information system (Szymanski and Hise, 2000). It is referred to as a feeling or attitude toward activities. Lee and Lehto (2013) discovered that behavioral intention was significantly influenced by user satisfaction. Liaw (2008) also established a positive relationship between user satisfaction and Behavioral Intention to use an electronic system. The present study applied user satisfaction as an external variable to investigate the relationship between user satisfaction and Behavioral Intention to use e-resources. On the other hand, the association between PU and user satisfaction was also examined in this study. Based on the literature findings, the study proposes the following two hypotheses:

(H4): PU has a positive effect on user satisfaction

(H6): User satisfaction has a positive effect on Behavioral Intention to use e-resources.

Method of learning and perceived ease of use of e-resources

Open and distance learning is the main mode of learning adopted by the OUSL. Hence, method of learning was modeled as a direct predictor of ease of use of e-resources, assuming that the mode of learning, namely, open and distance learning, facilitates students to access e-resources more easily. The learning method is an educational institution-based characteristic, which has been rarely used in technology acceptance model-related research studies. However, the importance of e-resources for distance learning has been addressed by several other research studies. Ankrah and Atuase (2018) noted that host institutions should have well maintained e-libraries since e-resources are the key source of information for distance learners. Tekale and Dalve (2012) pointed out that e-resources have helped distance learners across the world to gain quick and easy access to information, easy navigation with different search options, easy citations of scholarly works, uploading and updating of information, storing and disseminating information and many other advantages such as flexibility, time, space, cost effectiveness and ease of archiving. Considering the benefits of e-resources for distance learning, this research assumes that students engaged in physically distant learning are more likely to accept e-resources by perceiving that e-resources are easy to use. Thus, the study hypothesizes that:

(H5): Method of learning has a positive effect on the PEU of e-resources

Perceived usefulness and perceived ease of use of e-resources

PU and PEU are two of primary constructs of the TAM model. Davis (1989) defined PU as “the degree to which a person believes that using a particular system would enhance his or her job performance” and PEU as “the degree to which a person believes that using a particular system would be free of effort.”

Almarashdeh and Alsmadi (2016) carried out a study to investigate the acceptance of technology in distance learning programs, and discovered that PU and PEU have a significant effect on user behavior. Kim (2005), by comparing and contrasting the impact of PU and PEU on user acceptance of web-based subscription databases, showed that PU has a stronger effect on user acceptance than Ease of Use. Shih (2004) discovered that PEU is the strongest determinant of user attitude toward Internet use. Dumpit and Fernandez (2017) found that PU and PEU are strong predictors of usage behavior when students use social media. Tao (2009) mentioned the direct impact of PU and the indirect impact of PEU on both Behavioral Intention and actual behavior. Therefore, the study proposes the following hypotheses to evaluate the relationships between PU, PEU, Behavioral Intention to use e-resources, and ATT:

(H7): PU has a positive effect on ATT

(H8): PU has a positive effect on Behavioral Intention to use e-resources

(H9): PEU has a positive effect on the PU of e-resources

(H10): PEU has a positive effect on ATT

(H11): ATT has a positive effect on Behavioral Intention to use e-resources.

Research methodology

This study used the quantitative approach in evaluating the factors affecting behavioral intention to use e-resources by distance learners of the OUSL.

Population and sampling

The population of this study was the active undergraduates of 2020 who registered in any faculty of the OUSL, namely, the Faculty of Education, Faculty of Engineering Technology, Faculty of Health Sciences, Faculty of Humanities and Social Sciences, Faculty of Natural Sciences, and Faculty of Management Studies. Of the target population of active undergraduates of the OUSL (N = 26,503), a sample of 379 undergraduates was selected based on the Krejcie and Morgan table by considering the rules of thumb of Roscoe (1975) as cited in Hill (1998), and the key requirements of PLS-SEM. Sampling was carried out using the stratified random sampling technique to collect data from students chosen from all six (06) faculties of the OUSL.

Instrument

Data collection was administered mainly by using an online questionnaire designed in English using Google Forms. It comprised closed-ended questions to identify predictors that are expected to influence behavioral intention toward e-resources usage using TAM. These questions were established on a five-point Likert scale that ranged from “strongly disagree” to “strongly agree.” Indicators for each variable were developed by examining previous relevant research studies, and were altered slightly to comply with the requirements of the current study (Al-Aulamie, 2013; Kim, 2005; Kripanont, 2007; Lee and Lehto, 2013). The study’s data collection was done through an online survey method.

Data analysis

Data were carefully examined to identify missing values, errors, spelling mistakes, and duplicate responses after inserting them into an Excel sheet. These errors were rectified to ensure the completeness, consistency, and readability of the data. Data were imported into the SPSS IBM version 21, and the data screening and cleaning were carried out again to ensure that there were no errors. SEM was adopted to evaluate the proposed model, examining multiple statistical relationships simultaneously through visualization and model validation. Based on a few facts, PLS-SEM was selected after comparing it with Covariance-based Structural Equation Modeling (CB-SEM). CB-SEM is generally applied if the research objective is theory testing and confirmation; in contrast, PLS-SEM is the appropriate method for prediction and theory development (Dash and Paul, 2021). Second, compared with CB-SEM, PLS-SEM is used to assess latent variables with non-normal statistical distributions (Hair et al., 2014).

Further, PLS-SEM is more suitable for theory exploration and model evaluation with its associated features. PLS-SEM has become one of the most popular multivariate analytical methods as it works efficiently with small sample sizes, enables complex models, and practically no assumptions are required about the primary data (Hair et al., 2014). It is widely used for multivariate data analysis among business and social science scholars (Memon et al., 2021). Moreover, PLS-SEM has been developed to deal with data inadequacy issues such as heterogeneity. It also provides the researcher with suitable means to conduct a simultaneous test for multiple relationships among the variables in the case of complex and multivariate phenomena (Hair et al., 2014). The study used the SmartPLS version 3.3.3 statistical package for model evaluation. The level of significance of each variable was set at p < 0.05.

Results and discussion

Missing data identification was carried out as the first step before beginning multivariate analysis, since it is an important requirement in PLS-SEM. Therefore, missing values of the Likert scale responses were filtered, and the number 99 was entered into the empty cells to indicate the missing value. Testing of the research model was carried out in two (02) stages (1) measurement model evaluation and (2) Structural model estimation.

Measurement model evaluation

The validity and reliability of the constructs were used for model evaluation. Reliability is a criterion used in testing and evaluating measurements of variables to ensure the quality of data and the overall accuracy of the study results. Lancaster (2005) mentioned that reliability explains the extent to which a particular data collection instrument will produce the same results on different occasions. On the other hand, Saunders et al. (2019) stated that validity is “the appropriateness of the measures used, accuracy of the analysis of results, and generalizability of the findings.”

All Cronbach’s alpha values were greater than 0.800 (Table 1), indicating that the data collection instrument is reliable. The internal consistency of the indicators of each construct was assessed using composite reliability, and all scores were greater than 0.8 (Table 1). Therefore, the measurements were confirmed with adequate reliability.

Table 1.

Reliability analysis.

Construct	Indicators	Cronbach’s alpha	Composite reliability	AVE
Attitude toward Using (ATT)	ATT1. I like to use e-resources	0.95	0.97	0.91
	ATT2. I have a positive impression toward the use of e-resources
	ATT3. I believe that using e-resources would be beneficial for me
Behavioral Intention to Use (BI)	BI1. I would use e-resources for learning tasks in the future	0.95	0.96	0.90
	BI2. I will recommend others to use e-resources for learning
	BI3. I intend to increase the use of e-resources in the future
Computer Self Efficacy (SE)	SE1. I feel confident when I use the internet to obtain information	0.83	0.89	0.66
	SE2. I can use the internet even if there is no one to guide me on how to use it
	SE3. I can handle the technical errors that occur in using the internet on my own
	SE4. I can find the required information successfully using the internet
Method of Learning (ML)	ML1. It is easy to use e-resources with the open and distance education mode	0.92	0.94	0.80
	ML2. The geographical distance to the university encourages me to use e-resources
	ML3. E-resources are the best information tools for distance learning
	ML4. E-resources gave me a good approach to balancing my career and learning
Perceived Ease of Use (PEU)	PEU1. I could find the required information online easily	0.91	0.94	0.79
	PEU2. I could access the required information online easily
	PEU3. The high availability of e-resources allows me to obtain the information I want
	PEU4. It is easy to become skillful in searching and retrieving e-resources
Perceived Usefulness (PU)	PU1. E-resources would improve my learning performance	0.92	0.95	0.86
	PU2. E-resources enable me to accomplish my academic/ learning tasks quickly
	PU3. E-resources could make it easier to study course content
Relevance(R)	R1. The internet always provides relevant information required for learning	0.86	0.91	0.71
	R2. I could collect updated e- information to meet my learning tasks
	R3. I could find very useful information online
	R4. I could collect a sufficient amount of information that I needed
Social Influence (SI)	SI1. I am used to referring to e-resources because my peers also use them	0.83	0.88	0.59
	SI2. The staff of my university encourages me to use e-resources
	SI3. My friends taught me about e-resources
	SI4. People whose opinions I value prefer me to use e-resources
	SI5. Overall, using e-resources for learning will improve my social image in the university
User Satisfaction (US)	US1. I am satisfied with my overall experience of using e-resources for learning	0.90	0.93	0.77
	US2. I am happy about my overall experience of using e-resources for learning
	US3. I am satisfied with the availability of the required information online
	US4. My choice to use e-resources for learning is a good initiative

Convergent validity is the “extent to which a measure correlates positively with alternative measures of the same construct” (Hair et al., 2014). Convergent validity was evaluated using Average Variance Extracted (AVE) values. According to Hair et al. (2014), an AVE value of 0.50 or higher is recommended. According to Table 1, the minimum value of AVE is 0.59, which indicates that AVE values meet the recommended level. Thus, reliability and convergent validity were established.

Next, discriminant validity was evaluated, which measures the extent to which a construct is truly different from other constructs by empirical standards. According to the outerloadings and cross-loadings (Table 2) and the Fornell-Larcker criterion (Table 3), the construct’s discriminant validity has been established. Table 2 indicates that each indicator’s load is higher on its associated construct than on all other constructs, proving adequate discriminant validity.

Table 2.

Outer loadings and cross-loadings.

	ATT	BI	SE	ML	PEU	PU	R	SI	US
ATT1	0.95	0.83	0.64	0.77	0.72	0.69	0.66	0.42	0.66
ATT2	0.95	0.78	0.67	0.76	0.74	0.68	0.65	0.42	0.63
ATT3	0.96	0.83	0.66	0.78	0.72	0.70	0.67	0.43	0.67
BI1	0.81	0.96	0.63	0.76	0.66	0.66	0.67	0.42	0.63
BI2	0.81	0.96	0.65	0.75	0.67	0.68	0.66	0.44	0.66
BI3	0.81	0.94	0.60	0.74	0.65	0.64	0.67	0.47	0.65
EFFI1	0.61	0.56	0.76	0.51	0.59	0.50	0.67	0.44	0.56
EFFI2	0.64	0.61	0.87	0.60	0.65	0.62	0.63	0.28	0.63
EFFI3	0.40	0.40	0.75	0.43	0.44	0.41	0.42	0.25	0.41
EFFI4	0.55	0.55	0.86	0.56	0.65	0.53	0.62	0.36	0.60
MTD1	0.75	0.73	0.62	0.91	0.72	0.65	0.66	0.39	0.69
MTD2	0.58	0.60	0.46	0.82	0.56	0.56	0.54	0.39	0.56
MTD3	0.74	0.71	0.60	0.92	0.68	0.63	0.60	0.35	0.57
MTD4	0.79	0.78	0.64	0.93	0.70	0.71	0.68	0.40	0.63
PEU1	0.70	0.64	0.68	0.68	0.92	0.61	0.68	0.42	0.67
PEU2	0.67	0.61	0.65	0.67	0.91	0.62	0.66	0.41	0.69
PEU3	0.66	0.61	0.59	0.66	0.88	0.61	0.58	0.38	0.64
PEU4	0.67	0.61	0.66	0.63	0.85	0.55	0.66	0.45	0.70
PU1	0.71	0.68	0.66	0.70	0.69	0.94	0.73	0.37	0.62
PU2	0.64	0.62	0.57	0.64	0.62	0.92	0.63	0.32	0.55
PU3	0.66	0.64	0.56	0.65	0.61	0.93	0.71	0.35	0.58
REL1	0.41	0.42	0.50	0.43	0.52	0.42	0.70	0.32	0.46
REL2	0.59	0.62	0.61	0.63	0.63	0.64	0.89	0.47	0.64
REL3	0.67	0.66	0.67	0.65	0.66	0.73	0.89	0.46	0.67
REL4	0.63	0.63	0.65	0.61	0.63	0.67	0.87	0.49	0.61
SOC1	0.18	0.18	0.19	0.17	0.24	0.13	0.25	0.62	0.25
SOC2	0.39	0.39	0.38	0.32	0.35	0.32	0.43	0.79	0.41
SOC3	0.31	0.34	0.24	0.30	0.30	0.25	0.36	0.80	0.33
SOC4	0.37	0.40	0.34	0.35	0.42	0.31	0.42	0.86	0.47
SOC5	0.38	0.40	0.34	0.41	0.42	0.33	0.46	0.74	0.39
USER1	0.61	0.60	0.61	0.57	0.63	0.54	0.60	0.43	0.89
USER2	0.59	0.58	0.60	0.57	0.66	0.53	0.60	0.42	0.91
USER3	0.51	0.50	0.54	0.54	0.65	0.48	0.63	0.41	0.82
USER4	0.67	0.69	0.65	0.68	0.71	0.62	0.66	0.48	0.88

Bold values represent the each indicator’s load on its associated construct.

Table 3.

Fornell-Larcker criterion results of each construct. .

	ATT	BI	SE	ML	PEU	PU	R	SI	US
ATT	0.95
BI	0.85	0.95
SE	0.69	0.66	0.81
ML	0.81	0.79	0.65	0.89
PEU	0.76	0.70	0.72	0.75	0.89
PU	0.72	0.69	0.64	0.71	0.67	0.90
R	0.69	0.70	0.72	0.70	0.73	0.75	0.84
SI	0.47	0.47	0.41	0.43	0.47	0.37	0.52	0.77
US	0.69	0.68	0.69	0.68	0.76	0.63	0.71	0.50	0.88

According to the Fornell-Larcker criterion, the square root of the AVE of each construct (values across the diagonal) should be greater than the correlations with other constructs (inter-correlations) to ensure adequate discriminant validity (Hair et al., 2014). Table 3 depicts that the AVEs are indeed greater than the corresponding correlation values of other constructs, ensuring that all the constructs met the condition of discriminant validity.

Therefore, it is summarized that the proposed model is acceptable, as validity and reliability tests ensure adequate reliability and validity of all the constructs of the developed model.

Estimation of the structural model

A set of indicators was used to measure the significance of each construct in the developed model. The developed model was a reflective measurement model. Model estimation was done using SmartPLS software through the PLS-SEM Algorithm to estimate path coefficients, and R squared values and bootstrapping to evaluate the significance of the correlations and the regressions.

According to the R squared values given in Table 4, independent and mediator variables relevant to each construct have a significant ability to explain each construct’s variation, except it for user satisfaction. A low level of R squared value for US indicates that PU alone has less capacity to explain the variation in user satisfaction. Overall, the developed model is acceptable in explaining the factors affecting behavioral intention to use e-resources by distance learners of the OUSL.

Table 4.

R square values of each TAM construct.

Construct	Independent and mediator variables	R Square	R Square as a Percentage
ATT	PU, PEU	0.661	66.1%
BI	US, ATT, PU	0.753	75.3%
PU	R, SI, SE, PEU	0.601	60.1%
PEU	ML	0.557	55.7%
US	PU	0.394	39.4%

The hypotheses were tested by evaluating the statistical significance of the path coefficients, using significance values (p values) resulting from the PLS-SEM Algorithm. These path coefficients represent the structural relationships among constructs. Of the 11 hypotheses developed, two were rejected even at the 0.1 significance level. Table 5 shows the path model results with each hypothesis statement and the decision on acceptance or rejection. The model is shown in Figure 3. These hypotheses are arranged in descending order of the standard coefficient value. H5; ML has a positive impact on PEU, β = 0.75 with p value 0.00 < 0.01. H11; ATT has a positive impact on BI, β = 0.66 with p value 0.00 < 0.01. H4; PU has a positive impact on US, β = 0.63 with p value 0.00 < 0.01. H1; R has a positive impact on PU, β = 0.51 with p value 0.00 < 0.01. H10; PEU has a positive impact on ATT, β = 0.50 with p value 0.00 < 0.01. H7; PU has a positive impact on ATT, β = 0.38 with p value 0.00 < 0.01. H9; PEU has a positive impact on PU, β = 0.24 with p value 0.00 < 0.01. H6; US has a positive impact on BI, β = 0.15 with p value 0.038 < 0.05. H2; SE has a positive impact on PU, β = 0.12 with p value 0.04 < 0.05. Two hypotheses statements are rejected even at the 0.1 significance level. H8; PU has a positive impact on BI, β = 0.12 with p value 0.12 > 0.10. H3; SI has a positive impact on PU, β = −0.05 with p value 0.25 > 0.1.

Table 5.

Structural equation analysis.

Hypotheses	Hypotheses path	Path Coefficient (β)	Standard Error	Significance	Status
H5	ML → PEU	0.75	0.04	0.00**	Accepted**
H11	ATT → BI	0.66	0.07	0.00**	Accepted**
H4	PU → US	0.63	0.06	0.00**	Accepted**
H1	R → PU	0.51	0.07	0.00**	Accepted**
H10	PEU → ATT	0.50	0.05	0.00**	Accepted**
H7	PU → ATT	0.38	0.06	0.00**	Accepted**
H9	PEU → PU	0.24	0.07	0.00**	Accepted**
H6	US → BI	0.15	0.07	0.04*	Accepted*
H2	SE → PU	0.12	0.06	0.04*	Accepted*
H8	PU → BI	0.12	0.07	0.10	Rejected
H3	SI→ PU	−0.05	0.05	0.25	Rejected

Significant at 0.01. *Significant at 0.05.

Figure 3.

Structural equation model.

Based on these findings, an extended model (Figure 4) was developed to explain behavioral intention to use e-resources by distance learners of the OUSL, using the TAM. This new model consisted of four external variables; relevance of information, SE, method of learning, and user satisfaction, and 04 TAM constructs; PU, PEU, ATT, and Behavioral Intention to use.

Figure 4.

Developed model showing factors affecting intention to use e-resources.

Discussion

The use of e-resources is a great solution for distance learners to meet their information needs in line with technological advancement. This study presents a model explaining the factors affecting behavioral intention to use e-resources by distance learners of the OUSL by applying the TAM. The results reveal that some TAM constructs, such as PU, PEU, and ATT, and some external variables (i.e. method of learning, relevance, user satisfaction, and self-efficacy) are significant predictors of behavioral intention to use e-resources.

The results indicate that the method of learning influences the PEU of e-resources, becoming the strongest determinant of PEU (Hypothesis 5). This implies that the distance method of learning encourages students’ intention to use e-resources. The positive effect of ATT on behavioral intention to use was identified as the second strongest relationship in the proposed model (Hypothesis 11), revealing user attitudes as an important predictor of intention to use e-resources. Hypothesis 4, “PU has a positive effect on user satisfaction” was the third strongest relationship. Further, it was discovered that PU indirectly impacted behavioral intention to use e-resources through user satisfaction as well. The next most significant relationship was the positive effect of the relevance of information on the PU of e-resources. This finding supplements the earlier findings of Kim (2005), which indicate the positive effect of job relevance on the PU of web-based subscription databases. Shih (2004) pointed out that the relevance of information needs strongly determines the PU of internet use. Similarly, Tella (2011) also reported relevance as a significant predictor of e-library acceptance.

In addition, PEU and SE were found to directly affect the PU of e-resources (Hypothesis 9 and 2). This confirmed SE as a predictor of behavioral intention to use, revealing that distance learners with appropriate skills in using computers intend to utilize e-resources. The relationship between SE and PU is in line with the discovery made by Lee and Lehto (2013) that SE is a significant predictor of PU. The study further supported Hypothesis statements 7 and 10, indicating the positive impact of PU and PEU on ATT as defined by Davis (1989) from the TAM. Hypothesis 6, “User satisfaction has a positive effect on behavioral intention to use e-resources,” was also discovered as significant, emphasizing that user satisfaction is an influential factor toward behavioral intention to use e-resources by distance learners. Empirical pieces of evidence have also demonstrated a significant relationship between user satisfaction and behavioral intention to use e-resources (Lee and Lehto, 2013; Liaw, 2008).Two Hypothesis statements, 3 and 8, were insignificant, indicating no significant impact by the external variable “SI” on PU, which is consistent with the finding of Kim (2005) that subjective norms do not affect the intended use of Web-based subscription databases. However, SN as an insignificant predictor of behavioral intention to use reflects less community-based culture and communication among distance learners of the OUSL in information retrieval activities. The direct influence of PU on the behavioral intention to use e-resources was also not significant. Setiawan and Setyohadi (2018) also came up with a similar finding, that PU does not exert a significant impact on the behavioral intention to use e-resources. This result might mean that distance learners are not encouraged to use e-resources only based on the usefulness of these resources.

Similar to the relationships defined by Davis (1989) through TAM, the study also confirmed the positive effect of PU on ATT and the positive effect of PEU on ATT. However, PEU has a more influential effect on ATT than PU, that is, that distance learners are happier with the ease of use of e-resources than their usefulness. Confirming this finding, previous studies have reported that PEU influences ATT more than PU does (Setiawan and Setyohadi, 2018; Shih, 2004). The stronger effect of PEU on ATT explains that distance learners of the OUSL were more concerned about ease of use than usefulness when deciding to use e-resources.

A contradictory result to the relationships defined by Davis (1989) was discovered, as the direct relationship between PU and behavioral intention to use was found to be insignificant in the current study.

Conclusion, limitations, and future research

Access to information is a key requirement in following study programs in a distant mode, and e-resources play a significant role in meeting the information needs of distance learners. E-resources offer several benefits for distance learners by facilitating them to carry out their studies in their comfort zones. The study used an integrated approach and investigated the effect of some external variables and TAM constructs on the behavioral intention to use e-resources by distance learners of the OUSL. Using the findings, the study concludes that the distance mode of education strongly influences the behavioral intention to use e-resources. Distance learners of the OUSL perceive e-resources as easy to use with the learning method that they are engaging in. The findings also revealed that the relevance of information was an important factor considered by distance learners when using e-resources. Certain TAM constructs, namely, PU, PEU, and ATT, are important factors that significantly affect behavioral intention to use e-resources. This revealed that usefulness, fast and easy information retrieval, and the positive attitudes of distance learners enhance their intention to use e-resources. User satisfaction was also a significant variable. The weak, positive relationship between PU and SE revealed that the distance learner’s level of ability in using computers to seek information made less impact on PU.

SI has no significant impact on behavioral intention to use e-resources by distance learners of the OUSL. This implied that the influence of peers and lecturers does not significantly affect learners’ information behavior.

Accordingly, a new model was developed, which retains the basic structure set out by Davis (1989) but introduces new external factors to explain the behavioral intention to use electronic resources by distance learners of OUSL. The developed model recognizes the factors that encourage distance learners’ intention to use e-resources and to what extent each factor influences intention to use. This is important in developing strategies to enhance e-resource usage and in determining the additional requirements needed in fulfilling the information needs of distance learners.

One of the limitations of the study is that the study was only carried out among active undergraduates of the OUSL, though the university offers study courses ranging from certificate level to postgraduate level in the distance mode. Therefore, future research can be done by extending the same study including diploma-level students and postgraduate students of the OUSL, in order to broaden the scope of the research and validate the model. Furthermore, the literature suggested many factors which affect the behavioral intention to use e-resources. Investigating the effect of demographic variables, such as age, gender, educational level, and income, on the behavioral intention to use e-resources would also be useful and important.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Irosha Kumari Marasinghe

Author biographies

Irosha Kumari Marasinghe is a Senior Assistant Librarian of the Open University of Sri Lanka, Nawala,Nugegoda, Sri Lanka. Her research interests include library user behavior, bibliometric studies, knowledge management, and statistics.

Weerasooriya Arachchillage Weerasooriya is a senior professor in Library and Information Science, Department of Library and Information Science, Faculty of Social Sciences, University of Kelaniya, Sri Lanka. His research interests include Bibliographic control, Information Management and Knowledge, Library management, Indigenous Knowledge and Intangible Cultural Heritage Management, Library Advocacy, Bibliometrics and Scientometrics, Library and Information Ethics.

Nadeeka Rathnabahu is a senior lecturer of the Department of Library and Information Science, Faculty of Social Sciences, University of Kelaniya, Sri Lanka. Her research interests are ICT applications in Libraries, Library Conservation and Preservation.

References

Ahituv

(1980) A systematic approach toward assessing the value of an information system. MIS Quarterly 4(4): 61–75.

Ajzen

(1991) The theory of planned behavior. Organizational Behavior and Human Decision Processes 50(2): 179–211.

Al-Aulamie

(2013) Enhanced technology acceptance model to explain and predict learners’ behavioural intentions in learning management systems. PhD Thesis, University of Bedfordshire, Luton. Available at: https://uobrep.openrepository.com/handle/10547/323773 (accessed 22 January 2020).

Almarashdeh

Alsmadi

(2016) Investigating the acceptance of technology in distance learning program. In: 2016 IEEE and IFIP international conference in central Asia on information science and communications technologies ICISCT, Tashkent, Uzbekistan, 2–4 November 2016, pp.1–5. Tashkent: Tashkent University of Information Technologies.

Ankrah

Atuase

(2018) The use of electronic resources by postgraduate students of the University of Cape Coast. Library Philosophy and Practise. Epub ahead of print 07 February 2018. Available at: https://digitalcommons.unl.edu/libphilprac/1632/ (accessed 18 March 2020).

Bailey

Pearson

(1983) Development of a tool for measuring and analyzing computer user satisfaction. Management Science 29(5): 530–545.

Boadi

Letsolo

(2004) Information needs and information seeking behaviour of distance learners at the Institute of extra-mural studies in Lesotho. Information Development 20(3): 189–199.

Byrne

Bates

(2009) Use of the university library, elibrary, VLE, and other information sources by distance learning students in University College Dublin: Implications for academic librarianship. New Review of Academic Librarianship 15(1): 120–141.

Daramola

(2016) Perception and utilization of electronic resources by undergraduate students: The case of the Federal University of Technology Library, Akure. American Journal of Educational Research 4(5): 366–370.

10.

Dash

Paul

(2021) CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technological Forecasting and Social Change 173(3): 121092.

11.

Davis

(1986) A technology acceptance model for empirically testing new end-user information systems: Theory and results. PhD Thesis, Massachusetts Institute of Technology, Cambridge.

12.

Davis

(1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 13(3): 319–339.

13.

Davis

Bagozzi

Warshaw

(1989) User acceptance of computer technology: A comparison of two theoretical models. Management Science 35(8): 982–1003.

14.

DeLone

McLean

(1992) Information systems success: The quest for the dependent variable. Information Systems Research 3(1): 60–95.

15.

Dumpit

Fernandez

(2017) Analysis of the use of social media in Higher Education Institutions (HEIs) using the technology acceptance model. International Journal of Educational Technology in Higher Education 14(1): 5–16.

16.

Farahat

(2012) Applying the technology acceptance model to online learning in the Egyptian universities. Procedia - Social and Behavioral Sciences 64: 95–104.

17.

Gunasekera

(2010) Usage of virtual resource centre of the Open University of Sri Lanka: distance learner experience. In: Katariya

Ambu

JPK

Ram

(eds) 2nd international symposium on Emerging technologies and change in dimension of libraries and information services ETTLIS, Waknaghat, India, 3–5 June 2010, pp.252–256. Waknaghat: Jaypee University of Information Technology.

18.

Gunasekera

(2012) An investigation on use of e-information resources by distance learners at CRC computer lab of the Open University of Sri Lanka. In: 3rd international symposium on emerging trends and technologies in libraries and information services ETTLIS, New Delhi, India, 26–27 October 2012, pp.569–578. Waknaghat: Jaypee University of Information Technology.

19.

Gunasekera

(2014) Use of online learning resources: Experiences explored by distance learners at Matara Regional Centre of the Open University of Sri Lanka. E-Library Science Research Journal 2(4): 1–12.

20.

Hair

Hult

GTM

Ringle

, et al. (2014) A Primer on Partial Least Squares Structural Equation Modeling. Thousand Oaks, CA: SAGE Publishing.

21.

Hanif

Jamal

Imran

(2018) Extending the technology acceptance model for use of e-learning systems by digital learners. IEEE Access 6: 73395–73404.

22.

Hill

(1998) What sample size is “enough” in internet survey research. Interpersonal Computing and Technology 6(3–4): 1–10.

23.

Hong

Thong

JYL

Wong

, et al. (2002) Determinants of user acceptance of digital libraries: An empirical examination of individual differences and system characteristics. Journal of Management Information Systems 18(3): 97–124.

24.

Izuagbe

Hamzat

Joseph

(2016) Electronic information resources (EIR) adoption in private university libraries: The moderating effect of productivity and relative advantage on perceived usefulness. Journal of Information Science Theory and Practice 4(1): 30–48.

25.

Karahanna

Straub

(1999) The psychological origins of perceived usefulness and ease-of-use. Information Management 35(4): 237–250.

26.

Karunarathna

(2015) Use of electronic resources by law degree students at Anuradhapura Regional Centre of the Open University of Sri Lanka. Journal of the University Librarians Association of Sri Lanka 18(2): 41–61.

27.

Katz

(2002) Attitudes affecting college students’ preferences for distance learning. Journal of Computer Assisted Learning 18(1): 2–9.

28.

Kelley

Orr

(2003) Trends in distant student use of electronic resources: A survey. College & Research Libraries 64(3): 176–191.

29.

Kher

Downey

Monk

(2013) A longitudinal examination of computer self-efficacy change trajectories during training. Computers in Human Behavior 29(4): 1816–1824.

30.

Kim

(2005) User acceptance of web-based subscription databases: extending the technology acceptance model. PhD Thesis, The Florida State University, Florida. Available at: https://diginole.lib.fsu.edu/islandora/object/fsu:181256/datastream/PDF/download (accessed 25 November 2019).

31.

Kripanont

(2007) Examining a technology acceptance model of internet usage by academics within Thai business schools. PhD Thesis, Victoria University, Australia. Available at: http://vuir.vu.edu.au/1512/1/Kripanont.pdf (accessed 22 January 2020).

32.

Lancaster

(2005) Research Methods in Management - A Concise Introduction to Research in Management and Business Consultancy. Oxford: Elsevier Butterworth-Heinemann.

33.

Lee

Lehto

(2013) User acceptance of YouTube for procedural learning: An extension of the technology acceptance model. Computers & Education 61(1): 193–208.

34.

Liaw

(2008) Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & Education 51(2): 864–873.

35.

Liu

Yang

( (2004) Factors influencing distance-education graduate students’ use of information sources: A user study. The Journal of Academic Librarianship 30(1): 24–35.

36.

Memon

Ramayah

Cheah

, et al. (2021) PLS-SEM statistical programs: A review. Journal of Applied Structural Equation Modeling 5(1): i–xiv.

37.

Parsons

(2010) Information provision for HE distance learners using mobile devices. The Electronic Library 28(2): 231–244.

38.

Saunders

MNK

Lewis

Thornhill

(2019) Research Methods for Business Students, 8th edn. New York, NY: Pearson.

39.

Setiawan

Setyohadi

(2018) Understanding customers’ intention to use social network sites as complaint channel: An analysis of young customers’ perspectives. E3S Web of Conferences 31: 1–7.

40.

Shih

(2004) Extended Technology Acceptance Model of internet utilization behavior. Information Management 41(6): 719–729.

41.

Szymanski

Hise

(2000) E-satisfaction: An initial examination. Journal of Retailing 76(3): 309–322.

42.

Tang

Tseng

(2013) Distance learners’ self-efficacy and information literacy skills. The Journal of Academic Librarianship 39(6): 517–521.

43.

Tao

(2009) Intention to use and actual use of electronic information resources: Further exploring Technology Acceptance Model (TAM). In: 2009 AMIA annual symposium, San Francisco, CA, 14–18 November 2009, pp.629–633. San Francisco, CA: AMIA Knowledge Center.

44.

Tarhini

Elyas

Akour

, et al. (2016) Technology, demographic characteristics and e-learning acceptance: A conceptual model based on extended technology acceptance model. Higher Education Studies 6(3): 72–89.

45.

Tekale

Dalve

(2012) E-Resources. Review of Research 1(4): 1–4.

46.

Tella

(2011) Predicting users’ acceptance of e-library from the perspective of Technology Acceptance Model. International Journal of Digital Library Systems 2(4): 34–44.

47.

The Open University of Sri Lanka (2019) Open and distance learning. Available at: https://ou.ac.lk/introduction-to-odl-system/ (accessed 25 March 2022).

48.

Thong

JYL

Hong

Tam

(2002) Understanding user acceptance of digital libraries: What are the roles of interface characteristics, organizational context, and individual differences? International Journal of Human-Computer Studies 57: 215–242.

49.

Tury

Robinson

Bawden

(2015) The information seeking behaviour of distance learners: A case study of the University of London international programmes. The Journal of Academic Librarianship 41(3): 312–321.