1.5. Theoretical Grounding of the Research
1.5.3. Theoretical framework
The foremost function of theory is to provide a framework for making sense of the researcher’s observations as to why the world is the way it is (Maxwell, 2013). The eModeration focus of this thesis lies at the intersection of HCI and IS. Technology Acceptance Models are fundamental in predicting users’ acceptance of technology based on the technology’s function, its ease of use and the benefits that arise out of its use (Tarhini et al., 2015). HCI on the other hand, includes subjective attributes into the design space which has traditionally been concerned with ease of use (Ardito et al., 2007). It was therefore necessary to interrogate current research in the fields of technology acceptance, IS and HCI, so as to position this research. The steps taken in guiding the development of a theoretical framework to support this thesis were to first outline the Technology Acceptance Models of interest and their general implementation of use. Secondly, two IS success models, namely, the original D&M Success Model and the updated D&M Success Model, were discussed. Thirdly, constructs from the field of Human-Computer Interaction (HCI) were presented. Lastly, the constructs applicable to the eModeration context from each of Technology Acceptance Models, IS Success Models, and HCI were included in the theoretical framework underpinning this study.
1.5.3.1. Technology Acceptance Models
Technology acceptance models predict adoption decisions of information technologies in the workplace (Singh & Mansotra, 2019). In line with Blythe et al's. (2007, p. 4) view that UX is
“complementary to technology acceptance models”, the TAM, UTAUT, TOE, TTF, and HOT- Fit models were considered for applicability to the eModeration context (see Section 3.4).
These models are briefly outlined in Table 1-2 in terms of their usage context, basic premise behind each model, the limitations (if any), and the constructs applicable to the eModeration context. A more detailed discussion of these models is presented in Chapter Three (see Section 3.4).
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Table 1-2: Technology acceptance models
Model Usage contexts Basic premise Limitations Constructs applicable to eModeration
Technology Acceptance Model (TAM)
Business environments Education.
An individual’s intention to use a system is predicated by the belief that using the system will:
enhance their job performance (perceived usefulness); and
be effortless (perceived ease of use) (Venkatesh & Bala, 2008).
Excludes social, psychological, and organizational factors (Awa et al., 2016).
Ease of use
Usefulness.
Unified Theory of Acceptance and Usage of Technology (UTAUT) Model
Business environments Mobile commerce Education.
User adoption and usage is influenced by performance expectancy, effort expectancy, social influence, and facilitating conditions (Venkatesh et al., 2003).
Only examines the effects of the constructs on behavioural
intentions (Hariyanti et al., 2018; Lai, 2017).
Effort expectancy (learnability)
Performance expectancy (usefulness)
Facilitating conditions (user knowledge and support) (Ain et al., 2015;
Lai, 2017).
Technology- Organizational- Environmental (TOE) framework
Interorganizational systems
E-business Digital data interchange Cloud computing General applications (Korpelainen, 2011;
Borgman et al., 2013;
Awa et al., 2016).
Provides a theoretical framework for IT adoption.
Distinguishes between three contexts determining the adoption and implementation of technology, i.e.:
technology context,
organizational context,
environmental context (Borgman et al., 2013).
Focuses only on the fit between user and task. Ignores context (Mohamadali &
Garibaldi, 2012).
Technology characteristics
Task
characteristics.
Task-Technology Fit (TTF)
Cloud computing Mobile banking Wireless technology (Tripathi & Jigeesh, 2015; Yen et al., 2010; Zhou et al., 2010).
New technology will only be utilized if the functionality ‘fits’
the activity of the user (Röcker, 2010). Users will choose the technology that is most
appropriate for the task that they wish to accomplish.
TTF does not include aspects to establish the effectiveness of a system nor does it include the social context (Rai &
Selnes, 2019).
Task
characteristics
System functionality (Röcker, 2010).
HOT-Fit framework Health IS eGovernment eLearning.
A model for understanding the interrelated aspects of humans, organization, and technology (Erlirianto et al., 2015).
Human
Technology.
The following section presents an overview of the IS success models.
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1.5.3.2. IS Success Models
Delone and McLean (1992) proposed a model for operationalizing IS success. The original D&M IS success model provides a thorough understanding of IS success by distinguishing six interdependent variables thereof (system quality, information quality, IS use, user satisfaction, individual impact, and organizational impact) and describing the relationships between these variables (Delone & McLean, 1992). Seddon and Kiew (1996) subsequently suggested a change in focus from use to usefulness, indicating that usefulness is a better measure of IS success than use when system use is mandatory. This view has parallels with proven constructs used in technology acceptance models, where the concept of usefulness is equivalent to the idea of perceived usefulness in TAM (Petter et al., 2008). The updated D&M model includes service quality as a construct and replaces individual impact and organizational impact with net benefits so that the model can be applied to the most relevant level of analysis (DeLone &
McLean, 2003; Petter & McLean, 2009). IS Success Models are discussed further in Section 3.5.
1.5.3.3. Human-Computer Interaction (HCI)
HCI is a field of research that studies the design and use of information and computing technologies (ICTs) (Kuutti, 1995). Usability and user experience (UX), which are core components of HCI, are discussed in the following sections.
1.5.3.3.1. Usability
Usability refers to the quality of the user interface (Hariyanto et al., 2020). Amongst efforts to explain what the term means, usability has been described as the capability to be used by humans easily and effectively (Hornbæk, 2006); ‘‘quality in use’’ (Bevan, 2001, p. 541); and the ‘‘extent to which a system, product or service can be used by specified users to achieve specified goals with effectiveness, efficiency and satisfaction in a specified context of use’’
(ISO-IEC, 2018).
1.5.3.3.2. User experience (UX)
The term user experience (UX) has arisen from the realization that, as IS becomes more ubiquitous, users require more than just systems that are easy to use (Petrie & Bevan, 2009;
Lehong, 2020). People do not merely want to accomplish tasks, but also want to enjoy their
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interaction with an IT system. UX has thus emerged to encompass users’ interactions with, and reactions to, IT systems that go beyond the effectiveness, efficiency, and satisfaction measures as outlined by the ISO (Petrie & Bevan, 2009).
The ISO defines user experience as a person’s “perceptions and responses that result from the use and/or anticipated use of a system, product or service” (ISO-IEC, 2018). While usability focuses on user cognition and performance in human-technology interactions, UX highlights the non-utilitarian aspects of these interactions (Law et al., 2009). The user experience is therefore a holistic concept that includes all forms of emotional, cognitive, or physical reactions concerning the use of a system formed before, during, and after use (Hinderks et al., 2019).