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Establishing Determinants of Electronic Books Utilisation: An Integration of Two Human Computer Interaction Adoption Frameworks

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9733)

Abstract

Rapid technological developments led to the development of eBooks. The high propagation of pervasive technologies creates opportunities for eBook utilisation over traditional textbooks, thus providing students with learning resources everywhere and anywhere at a cheaper price. This study developed a model for assessing determinants for eBook adoption based on the Task-technology-Fit theory and the Technology Acceptance Model. The developed model is validated using factor analysis and path analysis statistical methods. Findings of the study suggest that usability of eBooks is influenced by learning task characteristics, technology characteristics and individual student characteristics. Furthermore, the study provides insights into the effects that eBooks adoption exerts on student academic performance.

Keywords

Task technology fit Technology acceptance model eBooks adoption 

1 Introduction

The hasty expansion and popularity of wireless Internet and mobile telecommunication technology has significantly contributed to the growth of electronic learning. The advent of digital content has altered many readers’ attention and perception towards the use of electronic books (eBooks).

Despite the availability of eBooks, most African countries are lagging behind on the adoption and use of eBooks for academic purposes. Allen and Kaddu [1] proclaim that, in spite of Africa’s remarkable growing interest and use of ubiquitous technologies, social media and blogs, it is surprising that eBooks are somewhat in their nascent phase. Therefore, it is necessary to establish factors that foster eBooks adoption and utilisation in Africa. This study aims to establish some of these factors that may influence eBook adoption and use among South African university students.

The field of technology adoption has largely been researched. Several Human Computer Interaction (HCI) researches have investigated determinants for technology use and acceptance [2, 3, 4]. The most common research frameworks that have been used for technology use and acceptance are the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT) model and the Task Technology Fit (TTF) theory. A limited number of studies have focused on eBooks adoption and use in Africa. Two HCI frameworks for technology acceptance and use, TAM (limited to user perceptions) and TTF (its scope does not include user perceptions) were amalgamated in this study in order to develop an appropriate integrated framework model (IFM) that may be used for eBook usability adoption.

The paper is structured as follows: Appropriate literature is discussed in line with eBooks, followed by a discussion of the key frameworks underpinning this study. Based on the literature, a conceptual framework will be developed leading to the research model of the study. The study then reports on the data collection instrument and the procedure used for collecting data. A correlational study procedure follows to present the study results. The study sums up with a discussion of the results and a conclusion is included.

2 Literature Review

The literature for the study is on eBooks adoption, TAM and TTF theory.

2.1 Technology Adoption Contextualized

Technology adoption is defined in terms of how well a given type of technology has been accepted by users (Davis et al., 1989). Venkatesh et al. [5] proclaim that adoption can be a resultant from a user’s perception of the technology after having used it. According to Goswami and Chandra [6], adoption refers to the acceptance and one’s willingness to use a given type of technology. De Silva et al. [7] denotes adoption as explaining decisions of persons by applying perceptive and social models of decision making. Shroff et al. [8] argue that adoption is a person’s conduct concerning using an information technology (IT). In this study, adoption refers to acceptance and use of eBooks for academic purposes.

According to Sarker and Valacich [9], the complexity of a technology may have serious implications for technology adoption. Chong et al. [10] proclaim that irrespective of higher institutions’ demands to implement eBooks in their academic libraries, literature has demonstrated the toil in perusing and reading an eBook using unfriendly user interfaces. According to Foasberg [11], an eBook is “a digital object with textual and/or other content which arises as a result of integrating the familiar concept of a book with features that can be provided in an electronic environment”. Kahn and Peter [12] identified features of eBooks as inclusive of the following: hypertext links, bookmarks, multimedia objects, interactive tools, annotations, highlights, and search and cross reference functions. Preliminary investigation indicates that students prefer to use eBooks due to their portability, financially affordability and easy of navigation on different electronic devices. Following this background, usability is defined in this study as the degree to which eBooks, learning and individual characteristics provides a fit suitable for promoting eBooks adoption.

2.2 Technology Acceptance Model

The TAM is the most prominent extensions of Azjen and Fishbein’s Theory of Reasoned Action (TRA) which was developed by Fred Davis in 1986. Davis et al. [13] TAM has been widely applied using its two major constructs; perceived usefulness (PU) and perceived ease of use (PEOU). The model suggests that when users are presented with a new technology, a number of user perception factors influence their decision about how and when they will use it [13]. The TAM is depicted in Fig. 1.
Fig. 1.

Technology acceptance model

(source: [2])

Previous studies indicate that TAM is the most influential model in technology usability and adoption [14, 15, 16]. Since this study aims to investigate eBook usability, it is appropriate to consider TAM constructs. Furthermore current studies have applied TAM in investigating eBook adoption [5]. According to Poon [17] the triumph of eBook adoption is dependent upon the application of a scholastic model that addresses scholar needs, relevant content and enhances student performance. Poon [17] utilised the TAM in investigating the intention of college students to use eBooks and the findings provided improved understanding on student conduct intention to adopt eBooks.

2.3 Task Technology Fit

Goodhue and Thompson [18] established this theory to examine the link concerning IT and individual performance. They anticipated confirming the supposition that usage and task-technology fit composed can better clarify the effect of IT with regards to performance than usage. According to Goodhue and Thompson [18], TTF ascertains that for a technology to have an encouraging influence on performance, it is necessary for the technology to be used and there should be a ‘good fit’ with the tasks it supports.

The constructs of TTF are, task characteristics, technology characteristics, TTF, performance impact and utilization. The TTF model is depicted in Fig. 2.
Fig. 2.

Task technology fit model

(source: [18])

In a study of mobile learning of information systems, Gebauer et al. [3] reported that the findings established that the TTF and usage together well clarified the influence of an IT on individual performance. Gebauer et al. [3] proclaimed that there was user-perceived accomplishment of individual tasks than usage alone. Lee et al. [19] proclaims that TTF is among the most widely used models for measuring performance enhancement using technology. This study seeks to establish the effect of eBooks usability on students’ performance, application of TTF seems to be appropriate for investigating this phenomena based on [19] claims mentioned above. D’Ambra et al. [15] suggest that the acceptance of eBooks shall be reliant on how academics remark the fit of eBooks toward the tasks they take on and what more significant functionality is provided by the technology that delivers the content.

2.4 The Conceptual Framework

Wentzel et al. [20] argue that TAM is limited in scope. Rogers (1995) and Wentzel et al. [20] warn researchers of the bias associated with studies that apply TAM since they concentrate solely on user perceptions as an independent factor necessary for technology adoption. Similarly, D’Ambra et al. [15] criticized TAM for its lack of task focus. The TTF has been criticized for lack of focus on individual perceptions affecting a user’s choices about technology [5].

An integration of TAM and TTF theory may result in a model strong enough to eliminate the weakness of both TAM and TTF and hence capitalizing on their strengths. The study’s contribution to the HCI body of knowledge involves the development of an integrated model for technology adoption. The study’s practical contribution may involve an improved eBooks adoption by the South African University students. Figure 3 represents the integrated IFM which incorporates the TAM and the TTF constructs.
Fig. 3.

Integrated framework model

The hypothesis for this study is listed below. The hypothesis indicates how the constructs in the IFM influence one another.

Task characteristics.

Goodhue and Thompson [18] define tasks as actions performed by a user in transforming inputs into outputs. Tasks can vary in a number of dimensions: task no routineness, task interdependence, and time criticality [15]. The attributes of these tasks form task characteristics. Task characteristics that may influence a student’s decision to depend more on eBooks and its associated technologies are of interest in this study [15]. The following hypothesis was therefore developed.

H1: Task characteristics have a positive influence on usability.

Technology characteristics.

Technology refers to tools in the form of hardware and software that can be used in performing a learning task [15, 18]. The attributes of these technologies can affect usage and users’ perception of the technology [15]. In this study technology involves eBooks and the eBooks readers used by the participants. We therefore develop the following hypothesis.

H2: Technology characteristics have a positive influence on usability.

Individual characteristics.

Individual characteristics encompass a user’s perceptions based on their attitude towards eBooks usage in learning. The individual characteristics are categorised into four dependent variables (PU, PEOU, Perceived enjoyment (PE), and Social influence (SI)) on an (independent variable) individual’s attitude towards use.

Perceived usefulness.

PU refers to the potential user’s subjective probability that applying a given type of technology will improve his or her task performance [13]. In this context PU refers to students’ subjective probability that using eBooks for academic purposes will enhance their learning performance. Therefore, the following hypothesis was formulated.

H3: PU has a positive influence on individual characteristics.

Perceived ease of use.

PEOU is defined as the degree to which the potential user expects the target system to be free of effort [13]. In this study PEOU refers to the extent to which university students expect the eBook system to be free of effort on its usage. We therefore formulated the following hypothesis.

H4: PEOU has a positive influence on individual characteristics.

Perceived enjoyment.

PE is defined as the degree to which using a computer system is perceived to be personally enjoyable in its own right, aside from the instrumental value of the technology [21]. In this study, PE refers to the extent to which the activity of using eBooks is perceived to be personally enjoyable on its own right aside from the instrumental value of education. Therefore the following hypothesis was developed.

H5: PE has a positive influence on individual characteristics.

Social influence.

SI is defined as the degree to which an individual perceives that important others believe he or she should use the new system [22]. In this context, SI refers to the extent to which a university student perceives that important others, like peers, family and lecturers believe he or she should use eBooks for learning purposes. Therefore the following hypothesis was developed.

H6: SI has a positive influence on individual characteristics.

Attitude towards use.

Attitude refers to a subjective or mental state of preparation for action. In this study, attitude towards use refers to a students’ mental state of preparation for using eBooks. Therefore following hypothesis was formulated.

H7: Attitude towards use of eBooks has a positive influence on usability.

Usability.

Usability refers to the degree that a particular type of technology assists the users in accomplishing particular objectives with efficiency and fulfilment [19]. In this study, usability refers to a perfect coordination among the learning task characteristics, eBooks characteristics and individual characteristics for the purposes of enhancing learning objectives. Therefore following hypothesis was formulated.

H8: Usability has a positive influence on adoption.

Adoption.

Adoption is an act that enables hesitant users to successfully accept and use technology [13]. In this study, adoption refers to the student’s decision to accept and use eBooks for learning purposes. Therefore following hypothesis was formulated.

H9: Adoption has a positive influence on performance enhancement.

Performance enhancement.

Performance refers to the accomplishment of a portfolio of tasks by an individual. Performance enhancement implies some mix of improved efficiency, improved effectiveness, and/or higher quality [18]. In this study performance enhancement refers to an improvement in effectively completing learning tasks using eBooks as a learning resource.

3 Methodology

3.1 Study Procedure

The participants for this study were IT Bachelor of Technology (B.Tech) students registered at a University of Technology in South Africa. This group of students was chosen because they all possess at least a laptop. The participants in this study used their eBook readers (e.g. laptops, Tablets, IPads and smartphones) for downloading and accessing the eBooks. A participant was allowed to use more than one gadget. The project targeted computer security subject (B.Tech subject). This subject was chosen because it has the highest number of students compared to other B.Tech subjects in the IT department. The study was conducted for a period of twelve weeks which constitute a full semester. It was conducted on the first semester in 2015. The university provided eBook licenses for computer security prescribed textbooks. Furthermore, the university provided software for downloading and accessing eBooks.

Participation was voluntary and participants were allowed to withdraw from the study at any stage. Students who chose to use traditional textbooks had an option of borrowing the textbook from the library or buy their personal copies. At the end of the semester data was collected from participants.

3.2 Participants

The study was conducted amid the time, in which the institution providing the context of the study weighed the possibilities of migrating from utilising traditional textbooks to eBooks. All the students registered in the targeted subject chose to participate in the study. Their ages ranges from 20–34. Among other things, the university provides Wi-Fi around campus to facilitate ubiquitous access of eBooks by students and staff. A total of 144 students participated in the study. The male participants were more than females. The most dominating race was Black African followed by mixed race commonly known as coloured in South African context. The detailed demographics of the participants are shown in Table 1.
Table 1.

Demographics

3.3 Instrument Development

The questionnaire instrument was grounded on the TTF and TAM constructs endorsed in [13, 15, 18]. A complete 42 questions survey questionnaire was constructed. The questionnaire utilised a 7 Likert scale ranging from “strongly agree” to “Strongly Disagree”. The variables measured include PU, PEOU, PE, SI, attitude toward use, task characteristics, technology characteristics, usability, adoption and performance.

3.4 Data Analysis

The statistical method used for this study was the structural equation model (SEM). The SEM is a general statistical modelling technique which is used in the studies of behavioural sciences, social science, studies of education and in other fields [23]. It is a combination of the factor analysis and the path analysis. SEM is used to determine whether a certain model is valid and analyses relationships among variables, hence used in this study to look at the research model and hypotheses. The SEM analysis is provided by linear structural relations (LISREL) and AMOS. Both LISREL and AMOS are software analysis tools for analysis covariance.

The LISREL is used in SEM for manifest and latent variables [24]. The LISREL is mainly syntax-based, although recent versions have featured a graphical user interface (GUI).

The AMOS enables one to specify, estimate, assess and present models to show hypothesised relationships among variables. The software lets one build models more accurately than with standard multivariate statistical techniques. It also allows one to build attitudinal and behavioural models that reflect complex relationships. The component analysing software, partial least squares (PLS) PLS-Graph and SmartPLS also provide SEM analysis. The PLS is a variable based technique. Hair, Sarstedt, Ringle and Mena [25] proclaim that PLS maximizes the explained variance of the endogenous latent variables by estimating partial model relationships in an iterative sequence of ordinal least regressions. A significant aspect of PLS is that it estimates latent variable scores as precise linear amalgamations of their related apparent variables and handles them as impeccable substitutes for apparent variables [25, 26]. The PLS has two sets of equations, the measurement model and the structural model.

4 Results

4.1 Measurement Model Analysis

The measurement model is comprised of equations representing the relationships between indicators and the variable measure [27]. It is used for examining all the measured variables. It also includes estimating the internal consistency for each block of indicators and evaluating construct validity. Internal consistency is evaluated using composite reliability (CR) and average variance extracted (AVE). The questionnaire instrument is validated in terms of reliability and construct validity.

Straub and Carlson [28] argues that construct validity examines whether the measures chosen are true constructs describing the event or merely artifacts of the methodology itself. If the constructs are valid in this sense, one can expect relatively high correlation between measures of constructs that are expected to differ [28]. According to Clark and Watson [29] reliability is an evaluation of measurement accuracy which is the extent to which the respondent can answer the same or approximately the same questions the same way each time. The questionnaire instrument included 40 items of which 3 were deleted. They were deleted they did not satisfy a loading item of at least 0.70 as specified in literature [27, 30, 31]. The remaining items that were examined represent the fundamental construct demonstrating to the content validity of the questionnaire instrument. Table 2 represents the results of the items loading, weights, composite reliability (CR) and average variance extracted (AVE).
Table 2.

Individuals loadings, weights, CR and AVE

Construct

Items

Item loadings

Construct CR

Construct AVE

Task

Characteristics (TsC)

TsC1

0.8210

 

0.634

TsC2

0.7903

0.836

 

TsC3

0.7817

 

TsC4

0.5807

Technology

Characteristics (TecC)

TecC1

0.8380

 

0.563

TecC2

0.8866

0.869

 

TecC3

0.7743

 

TecC4

0.8348

Perceived

Usefulness (PU)

PU1

0.8091

 

0.763

PU2

0.8736

0.957

 

PU3

0.7956

 

PU4

0.7687

Perceived Ease

of Use (PEoU)

PEoU1

0.7925

 

0.665

PEoU2

0.6210

0.821

 

PEoU3

0.8168

 

PEoU4

0.8133

Perceived

Enjoyment (PE)

PE1

0.9215

 

0.774

PE2

0.8331

0.943

 

PE3

0.8827

 

PE4

0.5209

Social

Influence (SI)

SI1

0.7306

 

0.584

SI2

0.7727

0.811

 

SI3

0.7947

 

SI3

0.7226

Attitude Towards

Use (ATU)

ATU1

0.8248

 

0.656

ATU2

0.7429

0.7985

 

ATU3

0.7772

 

ATU4

0.7111

Usability (U)

U1

0.8296

 

0.647

U2

0.9123

0.892

 

U3

0.7948

 

U4

0.7535

Adoption (A)

A1

0.8281

 

0.689

A2

0.8600

0.835

 

A3

0.9125

 

A4

0.7601

Performance (P)

P1

0.7236

 

0.734

P2

0.7602

0.861

 

P3

0.7157

 

P4

0.8711

Baum et al. (2001) proclaims that the foremost indicators for measuring convergent validity are CR and AVE. The results indicate a satisfactory CR for every latent variable since they are all over 0.70 [32]. The higher the CR, the higher the internal consistency of a latent variable. The AVE is greater than 0.5 which also adheres to the suggestions made by [32]. The higher the AVE, the higher is the convergent reliability. Table 2 depicts the individual loadings, weights, CR and AVE.

Chin [23] argues that, if the diagonal components demonstrating the square root of the AVE are suggestively greater than the off-diagonal values in the corresponding rows and columns, discriminate validity is confirmed. Following Chin [23] sentiments, our study confirms discriminate validity. The results for this study (see Table 3) depict good consistency because for each latent variable AVE values range between 0.7 and 0.9. This reveals that the latent variables in the study indicate a good convergent reliability. The bold diagonal numbers represent the square root of the AVE of each of the latent variables. Table 3 depicts the AVE for the study model constructs.
Table 3.

The average variance extracted (AVE) for the study model constructs

The study reveals satisfactory reliability and validity hence it is suitable to perform the hypotheses test concerning the correlations on the latent variables and predictability of the model’s explanatory power [14].

4.2 Structural Model Analysis

Structural model analysis is primarily performed for examining path coefficient and R2 within latent variables of the research model [33]. Zimmerman [33] proclaims that path coefficients measure the comparative forte and sign of casual relationship within latent variables. The R2 represents the total variance explained of independent variables on dependent variables. Predictability of the research model is also represented by R2. The R2 values are depicted on top of each variable. The R2 values of 42.30, 64.10, 58.30 and 74.20 have been recorded for attitude towards use, usability, adoption and performance enhancement respectively. The research model explained over 50 % of the total variance in usability and performance enhancement which supports the opinion that the research model holds a good predictability and explanatory power for the task-technology fit for the eBooks utilisation (Fig. 4).
Fig. 4.

Structural model

The path coefficients of each research model path per given hypotheses have been indicated. The study presented a total of 9 hypotheses. The results of the study indicate that all the 9 hypotheses were significant. The path coefficients for the 9 hypotheses were 42.20, 52.60, 44.70, 30.10, 33.70, 35.60, 32.00, 32.40 and 43.20.

5 Discussion

The results show that task characteristics and technology characteristics positively affected usability. These findings are consistent with [15, 18]. These findings suggest that students believe that learning characteristics such as self-study and sharing of learning resources influence usability of eBooks in higher education. Findings about technology characteristics suggest that features such as portability of eBooks, hyperlinks, and low costs contribute to eBooks usability. Task characteristics had stronger effects to usability than technology characteristics, suggesting that task characteristics are considered more important in the eBooks usability.

Considering the individual characteristics domain, findings of the study confirmed TAM related hypotheses. PU and PEOU positively affected students’ attitude to use eBooks. These finding supports previous studies’ reports [13, 14]. However, PU had a stronger effect on students’ attitude towards use compared to PEOU. This suggests that, the students’ mental state of preparation for learning using eBooks is strongly influenced by the extent they perceive that eBooks will improve their learning performance. Yet the degree to which the students expect the eBooks technology to be free of effort also contributes to students’ subjective preparation of learning using these technologies.

The study results further reveals that PE and SI positively affect students’ attitude towards eBooks usage. These findings imply that, apart from academic benefits students enjoy using eBooks. To them reading eBooks is just a fun activity. A strong relationship was revealed between SI and students’ attitude towards the use of eBooks. This relationship signifies that peers and lecturers played a significant role in preparing students’ mental states for learning using eBooks.

Students’ attitude towards the academic use of eBooks positively affected eBooks usability. These findings shed light that students believed that using eBooks can be beneficial to them and hence they perceived them as usable.

The model’s strongest relationship was revealed between eBooks usability and adoption. These findings suggest that a coordination among the learning task characteristics, eBooks characteristics and individual characteristics for the purposes of enhancing learning objectives influences hesitant students to successfully accept and use technology.

Another positive relationship was revealed between eBooks adoption and students performance enhancement, these findings are consistent with [15, 18]. The significance of these findings is that acceptance and use of eBooks improves students learning performance. These insights are vital for the continuous improvement of the instructional systems.

6 Conclusion

The correlational study reported in this paper sought to provide answers concerning suitable determinants for eBooks adoption. In providing these answers, the following contributions were made:
  • The study contributed to the human computer interaction (HCI) body of knowledge specifically in the eBooks adoption context by developing a model suitable for evaluating determinants for eBooks adoption.

  • The development and validation of an eBooks technology adoption based on the integration of the TTF theory and the Technology acceptance model.

  • The findings provided insights that eBooks acceptance and use may significantly contribute to students’ enhanced academic performance.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Faculty of Engineering and Information Communication TechnologyCentral University of TechnologyBloemfonteinSouth Africa
  2. 2.Melbourne Institute of TechnologyMelbourneAustralia

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