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CHAPTER THREE TECHNOLOGY ACCEPTANCE AND USAGE THEORIES/MODELS: THEIR APPLICABILITY TO OPEN ACCESS ADOPTION CONTEXT

Introduction

This Chapter reviews and discusses theories and models as a basis for formulating a suitable research model (theoretical framework) of the study. Specifically, the Chapter addresses the seventh research objective: to formulate a research model of technology acceptance regarding open access usage in research activities within Tanzanian public universities. Key determinants, which are also referred to as dependent variables in the research model expected to influence researchers’ open access behavioural intention and usage behaviour are proposed and discussed. Finally in the Chapter, the moderators that are expected to moderate the influence of such determinants are reviewed and discussed.

Models defined

A model is defined as a systematic description of a system, a theory or a phenomenon that accounts for its known or inferred properties and may be used for further study of its characteristics (The American Heritage Dictionary of the English Language, 2004). Similarly, according to Burch (2003: 266), a model is any abstract representation of some portion of the real world, constructed for the purpose of understanding, explaining, predicting or controlling a phenomenon being investigated. Burch identifies three types of models: 1) Physical models (e.g. a model of the hydrogen atom); 2) Visual models (e.g. a diagram of demographic transition); and 3) Theoretical models (e.g. the theory of evolution). From a scientific perspective, a model can therefore simply be referred to as a set of variables and their logical relationships constructed for the purpose of explaining a subject or the studied phenomenon.

Theories defined

According to The American Heritage Dictionary of the English Language (2004), a theory is defined as “a set of statements or principles derived to explain a group of facts or phenomena, especially the one that has been repeatedly tested or widely accepted and can be used to make the predictions about natural phenomena”. Similarly, Singleton, Straits and Straits (1993) as well as Powell and Connaway (2004) consider a theory as a set of explanatory variables used to describe the causal relationship for the occurrence of events under investigation. In short, a theory is an explanation based on observation, experimentation, and reasoning that has been tested and confirmed as a general principle to help explanation and prediction of phenomena. Theories are developed and tested to guide researchers on which relationships to observe, what variables are likely to affect what is being studied, and the conditions under which a causal relationship is likely to exist.

Similarities and differences between models and theories

From the above definitions of theories and models, it is evident that the two terms are closely related. The main difference between them is on the rigour to which each of them has been subjected in its testing and verification. In other words, a model needs further testing by empirical observation and experiments for it to qualify into a theory.
While a theory emanates from a systematic and formalised expression of previous empirical generalisations and experimental testing, a model need not necessarily be derived from empirical generalizations and testing (Burch, 2003). Indeed, according to Burch (2003: 280), “some authors distinguish theories and models by assigning the latter a role as intermediary between theory and empirical data but such a difference is regarded not fundamental”. Due to their relatedness, most technology acceptance studies have used the two terms interchangeably. For example, the most common technology acceptance theories and models are simply referred to as theories or models (Venkatesh et al, 2003; Peter, 2004; Kripanont, 2007; Wu, Tao and Yang, 2007). In this study, although models and theories are considered as playing the same role, the former is treated as an intermediary towards a theory development. In this respect therefore, a theory is considered to emanate from a model that has undergone repeated tests and validation to support empirical generalisations.

Theories and their role in the research process

Theories play a critical role in the research process from planning, data collection and explanation of the emerging findings. According to Whitworth (2007), theories propose and connect abstract constructs/variables, and research transforms them into the physical data. Researchers who proceed “without a theory or model rarely conduct top-quality research and frequently find themselves in quandary” while reporting their research findings (Neuman, 2006: 77). Theory direction, level of analysis, theory focus, and form of explanation are important aspects that need to be specified at early stages of the study to avoid confusion during data collection, analysis as well as theory testing (Klein, 1994; Neuman, 2006). The following subsections briefly describe the four theory aspects and specify how they are used in the context of this study.

Theory direction

Theory direction may either be deductive or inductive (Leedy and Ormrod, 2005; Neuman, 2006; Al-Qeisi, 2009). To theorise using the deductive approach, the researcher begins with “abstract concepts or a theoretical proposition and outlines the logical connection among concepts and then moves towards concrete evidence” (Neuman, 2006: 59). On the other hand, for the inductive approach, the researcher begins with collecting empirical evidence before developing theoretical concepts and propositions. It should be noted that in the deductive approach the researchers adopts more quantitative questions than qualitative questions while the opposite is true with respect to inductive theorising (Al-Qeisi, 2009). In this study, the deductive approach of theory direction was adopted with respect to testing and validation of the research model. This choice was motivated by the fact that data collection without theory guidance may lead to time and effort wastage for lack of research focus (Whitworth, 2007). According to Leedy and Ormrod (2005: 32), deductive reasoning is also “extremely valuable for generating research hypotheses and testing theories”. By employing the deductive approach in this study, only relevant data were collected unlike the inductive approach in which data are gathered with little focus at the beginning as the theory is applied towards the end of the research with the possibility of collecting some irrelevant data. Furthermore, this approach was useful in validating the research model as will be noted in the subsequent chapters of this thesis.
Semi-structured questions involving some open ended questions were also used for data gathering as will be noted in Chapter Four. This implies that to some extent inductive theorising was equally important in the study. This is true for data collected beyond those intended for the validation of the research model. Any mixed research method involving qualitative and quantitative approaches like this one has elements of both deductive and inductive theories (Al-Qeisi, 2009). Both deductive and inductive approaches were considered important in this study as the former “supplies the shape of the argument and induction establishes agreement about one or more stages of in the argument” (Al-Qeisi, 2009: 202)

Levels of analysis

There are three main levels of theories in social inquiry: micro, meso and macro levels. Neuman (2006 & 2007) describes the three levels of theorising as follows: Micro-level theory – focuses on the micro level of social life in short durations (e.g. face to face interactions among individuals or small groups); while macro-level theory focuses on the macro-level of social life (e.g. social institutions, major sectors of societies, or world regions) and the processes that occur over long durations (many years, multiple decades, or a century or longer); and meso-level theory focuses on the relations, processes, and structures at middle level of social life (e.g. organisations, movements, and communities) and events occurring over several months or years. Based on the nature of this study, micro-level theory was more appropriate as the study focuses on individual researchers for data gathering as well as units of analysis to assess the acceptance and usage of open access in public universities in Tanzania.

Focus of theory

The focus of a theory can either be substantive or formal. Substantive theory is a set of prepositions which furnish an explanation for an applied area of inquiry while a formal theory is general and applies across many disciplines (Glazier & Grover, 2002; Abdallah, 2005; Neuman, 2006). While substantive theory offers powerful explanations for a topic area as a result of being tailored to it and incorporating rich details “from specific settings, processes, or events, it is often difficult to generalize to different topic areas using such type of a theory” (Neuman, 2006: 62). On the other hand, formal theories help researchers to recognize and explain similar features that operate across several different topics but are difficult to apply to specific social settings unless adjustments are made to suit such contexts (Neuman, 2006; Abdallah, 2005). The goal of this research was to formulate a research model for open access scholarly communication in public universities in Tanzania based on the existing technology acceptance theories. The study therefore adopts the substantive theory focus for deeper understanding of researchers’ behavioural intention and usage of open access. The research model may be subjected to further validation by other studies for applicability to other research contexts.

Forms of explanation

There are basically three major forms of theoretical explanation that social scientists employ in explaining their research findings. They include: causal, structural, and interpretative (Neuman, 2006 & 2007). According to Neuman (2006: 63), “the causal explanation is a theoretical explanation about why events occur and how things work expressed in terms of causes and effects”. The structural explanation is about “why events occur and how things work expressed by outlining an overall structure and emphasizing location, interdependences, distances, or relations among positions in that structure” (Neuman, 2006: 69). Neuman (2007) further describes the structure in question to be like a wheel with spokes like the web with interconnected parts where aspects of social life are explained. A type of theoretical explanation about why events occur and how things work, expressed in terms of the socially constructed meanings and subjective worldviews is what is referred to as interpretative explanation. The interpretive theorist tries to comprehend or mentally grasp the operation of the social world without differing with the understanding of other people. According to Neuman (2006: 72), this type of explanation is about “why events occur and how things work expressed in terms of socially constructed meanings and subjective worldviews”. This research adopts the causal explanation. This type of explanation is considered appropriate for this study as it simplifies the understanding of the subject being investigated. As is the case under the current study, using this approach, researchers normally use diagrams to show the causal relations in order to present a simplified picture of the existing relationships of the variables under the study.

Technology acceptance theories and models

According to Louho, Kallioja and Oittinen (2006: 15), “technology acceptance is about how people accept and adopt some technology to use”. The main objectives of many technology acceptance studies are to investigate how to promote usage and also explain what hinders acceptance and usage of technologies (Kripanont, 2007). This is in line with the present study aiming at investigating the factors that affect the adoption of open access in Tanzanian public universities in order to device the means of promoting the adoption of the new mode of scholarly communication. A review of the existing technology acceptance models/theories is therefore important for the researcher to gather theoretical concepts that can be used as a basis in formulating a sound research model for this study.
A number of models/theories designed to facilitate the understanding of factors impacting the acceptance and use of technologies have been documented (Venkatesh et al, 2003; Kripanont, 2007; Barati and Mohammadi, 2009; Ghobakhloo, Zulkifli and Aziz, 2010; Jayasingh and Eze, 2010). The following are some of the well known technology acceptance models and theories:
¾ Theory of Reasoned Action (TRA);
¾ Motivational Model (MM);
¾ Theory of Planned Behaviour (TPB);
¾ Decomposed Theory of Planned Behaviour (DTPB);
¾ Technology Acceptance Model (TAM);
¾ Technology Acceptance Model (TAM2);
¾ Combined TAM and TPB (C-TAM-TPB);
¾ Model of PC Utilisation (MPCU);
¾ Social Cognitive Theory (SCT);
¾ Innovation Diffusion Theory (IDT) and;
¾ The Unified Theory of Acceptance and Use of Technology (UTAUT).
The above models and theories have been reviewed and analysed by several studies (Szajna, 1996; Clarke, 1999; Stacy and Sally, 1999; Lederer et al, 2000; Legris, Ingham and Collerette, 2003; Venkatesh et al, 2003; Gengatharen and Standing, 2004; Perez, et al, 2004; Rosen, 2005; Minishi-Majanja and Kiplang’ati, 2005; Kripanont, 2007; Al-Qeisi, 2009; Van Biljon and Renaud, 2009; Ghobakhloo, Zulkifli and Aziz, 2010; Jayasingh and Eze, 2010). From such reviews and analysis it is evident that each technology acceptance theory or model has different premises and benefits such that researchers are confronted with a choice among a multitude of theories/ models. Despite the specific advantages of each theory, the capability of a theory/model in predicting and explaining behaviour is measured by the extent to which the predictors in the theory could account for a reasonable proportion of the variance in behavioural intention and usage behaviour (Kripanont, 2007). According to Singleton, Straits and Straits (1993), a theory or a model should be judged superior to others if: 1) it involves the fewest number of statements and assumptions, 2) it explains the broadest range of phenomena, and 3) its predictions are more accurate. It should however be noted that while the fewest number of variables is desirable for a theory, its contribution to the understanding of the studied phenomena is equally crucial and hence a balance of the two aspects is quite important. For predictive, “practical application of the model, parsimony (few predictors) may be heavily weighed, on the other hand, if trying to obtain the complete understanding of phenomena, a degree of parsimony may be sacrificed” (Kripanont, 2007: 80).
Due to the existence of several competing technology acceptance theories and models, it has necessitated researchers to compare them in order to identify the most promising ones in respect of their ability to predict and explain individual behaviour towards acceptance and usage of technology. According to Kripanont (2007), most of such studies have made comparison of two or three theories. Contrary to most studies that made comparison of few models, studies reported by Venkatesh et al (2003) and Kripanont (2007) compared eight and nine models respectively. While a study by Kripanont (2007), like many other studies that compared technology acceptance models was based on literature review, Venkatesh et al (2003) compared the models based on empirical data. A study by Venkatesh et al (2003) can therefore be judged to have been a more realistic way of comparing the technology acceptance models.
Venkatesh et al (2003) compared 8 models to determine their ability to explain behavioural intention (the explained variance R2) based on empirical studies conducted at different times (T1 = immediate following training but prior to introduction of new technology; T2 = one month after introduction of the new technology; and T3 = three months after introduction of the new technology). Table 3.1 presents a summary of technology acceptance theories/models comparisons in terms of their key constructs, moderators and the explained variance.
From the comparison of models in Table 3.1, there are notable similarities and differences among technology acceptance models in terms of their constructs and moderators as well as their explanatory abilities as follows:
¾ Constructs (dependent variables) range from two (TRA and MM) to eight (IDT);
¾ Some models such as MM and SCT did not include moderators. The UTAUT model has the highest number of moderators (4);
¾ Experience is the most commonly used moderator among all theories/models that employed moderators;
¾ There is evidence that moderators can play significant role on the explanatory ability of models even under situations of similar constructs. For example, TPB employing different moderators changed the explanatory ability of different versions of the model in question from 0.36, 0.46 and 0.47 variances respectively;
¾ UTAUT model integrates constructs and moderators from across other eight technology acceptance theories/models and;
¾ The explanatory ability of technology usage intention in terms of variance ranged from 0.36 (TRA, SCT) lowest to 0.69 (UTAUT) highest.
From the above observations, it is evident that the UTAUT demonstrates the highest explanatory power in explaining behavioural intention and usage of technology.
Therefore, the theory in question contributes to better understanding about the drivers of behaviour of acceptance and use of new technologies than other similar theories and models (Venkatesh et al, 2003; Kripanont, 2007; Wu, Tao and Yang, 2007). UTAUT was therefore used as the main basis in formulating the research model of this study. The description and further justification for the suitability of the UTAUT model to this study is presented in the following sections.

The Unified Theory of Acceptance and Use of Technology

The UTAUT model was developed by Venkatesh and his team basing upon the conceptual and empirical similarities among eight competing technology acceptance models: TRA, TAM, MM, TPB, C-TAM-TPB, MPCU, IDT, and SCT (Venkatesh et al, 2003; Rosen, 2005; Schaper and Pervan, 2007; Birth and Irvine, 2009; Van Biljon and Renaud, 2009). The UTAUT model successfully integrated key elements from among the initial set of 32 main effects and four moderators from eight different models (Venkatesh et al, 2003; Peter, 2004; Kripanont, 2007). According to Venkatesh et al (2003), the UTAUT model was formulated by first, identification and discussion of eight specific models of the determinants of intention and usage of information technology; secondly, those models were empirically compared using longitudinal data from four organisations (Entertainment, Telecomm services, Banking, and Public administration); third, the conceptual and empirical similarities across the eight models were used to formulate the UTAUT model; and fourth, the UTAUT model was empirically tested using the original data from the above four organisations and then cross-validated using new data from additional two organisations (Financial services and Retail electronics).
From the theoretical perspective, the UTAUT model provides a refined view of how the determinants of intention and behaviour evolve over time. This model provides a useful tool for the management needing to assess the likelihood of success for technology introduction as well as helping to understand the drivers of technology acceptance so as to proactively design interventions including training targeted at populations of users that may be less inclined to adopt and use new technology (Venkatesh et al, 2003; Kripanont, 2007). As illustrated in Figure 2 (page 92), the UTAUT model postulates three indirect determinants of new technology usage (performance expectancy, effort expectancy, and social influence), and two direct determinants of usage behaviour (intention and facilitating conditions). Four moderators, gender, age, voluntariness, and experience were identified to play specific moderating roles to the indirect and direct determinants of technology use behaviour. The following subsections elaborate on the key determinants and moderators of the UTAUT model.

Indirect determinants of technology usage

Indirect determinants of technology usage are those factors that influence individuals to build interest toward technology usage. Such factors are briefly explained under the following sub-sections.

Performance expectancy

Performance expectancy is the degree to which an individual believes that the new innovation will help him or her to attain gains in job performance (Venkatesh et al, 2003). This concept is similar to perceived usefulness in the Technology Acceptance Model; Combined Technology Acceptance Model and Theory of Planned Behaviour; outcome expectations in Social Cognitive Theory; and as relative advantage for Innovations Diffusion Theory (Venkatesh et al, 2003). According to UTAUT model, it is expected that individuals will build interest of using a certain technology if they believe that it will enable them to improve their performance in what they are doing. This means that unless the new technology improves efficiency or quality of an individuals’ job, it is less likely to attract their interest on it. The relationship between performance expectancy and intention is moderated by age and gender such that performance expectancy directly affects intention of technology usage and is stronger for men and younger workers than it is for other categories of people (Venkatesh et al, 2003; Louho, Kallioja and Oittinen, 2006).

Effort expectancy

Effort expectancy is the degree of ease associated with the use of the system and is considered to be similar to “perceived ease of use (Technology Acceptance Model), Complexity (Model of PC Utilization), and Ease of Use for Innovation Diffusion Theory)” (Venkatesh et al, 2003: 450). The model postulates that individuals are likely to show interest in technology usage if that technology is easy to use. This means less complicated technologies can easily attract usage intention of many users than complicated technologies. Age, gender and experience are considered to play significant moderating roles for effort expectancy towards technology usage behavioural intention. Effort expectancy is said to influence behavioural intention and is stronger for women, older workers, and those with limited experience than for other categories of people.

Social influence

Social influence is defined as the degree to which an individual perceives it important that other people believe he or she should use the new system (Venkatesh et al, 2003). According to Venkatesh and his co-authors, this aspect is regarded as subjective norm in the theories of Reasoned Action, Technology Acceptance Model, Combined Technology Acceptance Model and Theory of Planned Behaviour; social factors in Model for PC Utilisation; and image for Innovation Diffusion Theory. Developers of this theory believe that individuals will be in a position to show interest in technology usage if their peers or superiors value and encourage them to use such technologies. In other words, individuals’ intention to use new technology is expected to be high if such individuals expect their peers will look positively at them if they use that technology. Social influence is moderated by gender, age, experience and voluntariness of use. The effect of social influence on behaviour intention is stronger for women, older workers, those with limited experience, and those using the system under mandatory conditions.

Direct determinants of technology usage

According to the UTAUT model, technology usage is subject to individuals building interest (behavioural intention) toward it. In other words, behavioural intention of an individual towards a technology will ultimately lead him/her to use the technology in question. In addition to behavioural intention, the UTAUT model also considers facilitating conditions as the other direct determinant of technology usage.
Facilitating conditions are defined as the degree to which an individual believes that an organisational and technical infrastructure exists to support the use of the system (Venkatesh et al, 2003). According to Venkatesh et al (2003: 453), this definition is also conceptualised as: “perceived behavioural control (Theory of Planned Behaviour); facilitating conditions (Model of PC Utilization); and compatibility (Innovation Diffusion Theory)”. In this model, it is postulated that the usage of technology is dependent on the availability of an enabling environment for its application. For example, computer applications may not be expected in an organisation without such facilities. The influence of facilitating conditions towards usage of technology is moderated by age and experience such that its effect is stronger for older workers and those with more experience. In other words, it is expected that older people would be less interested in adopting the technology than would be the case with young workers. The effect of facilitating conditions on technology usage is also expected to increase with experience “as users of technology find multiple avenues for help and support throughout the organisation, thereby removing impediments to sustained usage” (Venkatesh et al, 2003: 453).

TABLE OF CONTENTS
DECLARATION
ABSTRACT
ACKNOWLEDGEMENTS
LIST OF TABLES
LIST OF FIGURES
LIST OF ABBREVIATIONS
Chapter One: Background to the study
1.0 Introduction
1.1 Scholarly communication
1.2 Open access scholarly communication
1.3 Public universities in Tanzania
1.4 Problem statement
1.5 Aim and objectives of the study
1.6 Scope and limitations of the study
1.7 Significance of the study
1.8 Definition of key concepts
1.9 Overview of the research methodology
1.10 Thesis structure
1.11 Chapter summary
Chapter Two: Review of scholarly communication focusing on open access
2.0 Introduction
2.1 Developments in scholarly communication and open access adoption
2.2 The global awareness and usage of open access
2.3 Factors facilitating open access adoption
2.4 Factors hindering the adoption of open access in research activities
2.5 Scholars’ perceptions on open access repositories
2.6 Chapter summary
Chapter Three: Technology acceptance and usage theories/models: their applicability to open access adoption context
3.0 Introduction
3.1 Models defined
3.2 Theories defined
3.3 Similarities and differences between models and theories
3.4 Theories and their role in the research process
3.5 Technology acceptance theories and models
3.6 The Unified Theory of Acceptance and Use of Technology
3.7 The UTAUT model adoption by technology acceptance studies
3.8 Fitness of the UTAUT model in gender and IT theoretical perspectives
3.9 The proposed research model
3.10 Chapter summary
Chapter Four: Research methodology
4.0 Introduction
4.1 The scientific research process
4.2 The research purpose
4.3 The research approach
4.4 The research design
4.5 Ethical considerations
4.6 Chapter summary
Chapter Five: Presentation of findings
5.0 Introduction
5.1 Profile of the respondents
5.2 Open access awareness
5.3 Open access usage
5.4 Factors facilitating researchers’ use of open access in scholarly communication
5.5 Respondents’ general perceptions about open access
5.6 Respondents’ perceptions on institutional repositories’ establishment
5.7 Factors hindering researchers’ use of open access content
5.8 Factors hindering researchers’ dissemination of research findings through open access
5.9 Validation of the research model on open access usage in scholarly communication
5.10 Chapter summary
Chapter Six: Interpretation and discussion of research findings
6.0 Introduction
6.1 Respondents’ background
6.2 Awareness of the concept of open access
6.3 Usage of open access outlets in accessing scholarly content
6.4 Dissemination of scholarly output in open access outlets
6.5 Disciplinary differences in open access usage
6.6 Enablers of open access usage in scholarly communication
6.7 The need for institutional repositories’ establishment
6.8 Obstacles to open access usage
6.9 The emerging research model on open access scholarly communication
6.10 Chapter summary
Chapter Seven: Summary, conclusions and recommendations
7.0 Introduction
7.1 Overall summary of the study
7.2 Conclusions
7.3 Recommendations
7.4 Chapter summary
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