**2. Review of related literature**

## **2.1 Theoretical foundations of WSMA**

#### *2.1.1 The technology acceptance model*

A key theory widely used in information technology adoption literature is the Technology Acceptance Model (TAM). TAM was developed by Davis [14] to explain the user adoption of technology in organisations. TAM posits that two factors, perceived usefulness and perceived ease of use, are the two main determinants of system usage in organisations [15]. Furthermore, it is asserted that the systems designer has some degree of control on these two factors. In TAM, *Perceived Usefulness (PU)* is defined as the degree to which an individual believes that using a particular system would enhance his or her job performance, whereas *Perceived Ease of Use* (PEOU) is the degree to which an individual believes that using a particular system would be free of physical and mental effort [15]. In the application of TAM in WSMA in eCommerce environments, Mou and Benyoucef [16] applied TAM in a meta-analytic study that investigated consumer behaviour in social commerce which is an aspect of eCommerce. The authors [16] compared different theoretical frameworks and found the variables from TAM, in combination with other models to be useful in determining eCommerce adoption. Therefore, TAM can be applied in developing country contexts in SMEs environment to provide insights to managers in the decision-making process.

#### *2.1.2 The theory of planned behaviour*

The Theory of Planned Behaviour (TPB) was proposed by Ajzen [17] from the social psychology background. TPB posits that there are three constructs that predict intention to use an innovation [17]. These are attitude, subjective norm and perceived behavioural control. Attitude is formed from cognitive beliefs and refers to 'an individual's positive or negative feeling (evaluative affect) about performing the target behaviour' [18]. Subjective norm represents the social influences on behaviour and refers to the perception about whether others who are important to a person believe that he or she should engage in a particular behaviour [18]. Perceived behavioural

#### *Adoption of Web 2.0 Social Media eCommerce in SMEs: Conceptualising Theories and Factors… DOI: http://dx.doi.org/10.5772/intechopen.109604*

control represents the constraints on behaviour and refers to the 'perceived ease or difficulty of performing a behaviour' [18]. In application, Ghani et al. [19] studied cloud-based eCommerce services in Malaysian business owner-managers with the aim of understanding how their own behaviour could influence the usage intention. [19] investigated the influences of the Theory of Planned Behaviour (TPB) and Task-Technology Fit (TTF) towards textile cyberpreneur's intention to adopt cloudbased mobile retail application. It is reported that TTF and TPB constructs, attitude, subjective norm and perceived behavioural control have significant positive effects on textile cyberpreneur's behavioural intentions [19] of SMEs. Hence, TPB can be applied in developing country SMEs where Business-to-Business (B2B) eCommerce decisions-making processes are critical.

#### *2.1.3 The unified theory of acceptance and use of technology*

Technology acceptance theories have been applied in a variety of areas to understand and predict user's behaviour and acceptance a particular technology. The Unified Theory of Acceptance and Use of Technology (UTAUT) model was developed by Venkatesh et al., [20] as an amalgam of seven models used to study technology acceptance in different fields. According to [20], the theory integrates models such as the theory of reasoned action (TRA), technology acceptance model (TAM), motivational model, theory of planned behaviour (TPB), model of personal computer utilisation, diffusion innovation theory (DIT) and the Social Cognitive Theory (SCT). The UTAUT model uses four main constructs to predict behavioural intentions and use behaviour of technology in an organisation [20]. The main constructs of the model include Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI) and Facilitating Conditions (FC). UTAUT was further improved to UTAUT2 by including other conditions or constructs. UTAUT2 postulates that the use of technology by individuals is underpinned by the effect of the three additional constructs, which are, hedonic motive, cost/perceived value, and habit, moderated by age, gender and experience [20].

The application of UTAUT2 was undertaken by Arpaci et al., [21], who studied the social sustainability of the Metaverse by integrating the UTAUT2 constructs and five personality traits to understand the social sustainability of the Metaverse. The model was tested by employing a hybrid covariance-based structural equation modelling (CB-SEM) and artificial neural network (ANN) approach based on collecting data from 446 Metaverse users. The CB-SEM results showed that performance expectancy, social influence, hedonic motivation, price value, habit, agreeableness, neuroticism and openness significantly impact the social sustainability of the Metaverse, while no significant effect is reported regarding effort expectancy, facilitating conditions, conscientiousness and extraversion. Drawing on these findings, the study offers several theoretical contributions and sheds light on several practical implications for developers, designers and decision-makers promoting the use of the Metaverse [21]. In another context, Shoeib et al., [22] applied UTAUT2 by extended the UTAUT2 with perceived value, trust and a restructured social commerce construct. The study [22] utilised 463 surveys distributed in Qatar and analysed the data using Structural Equation Model. It is reported by [22] that the results fully supported the proposed model, where *trust, perceived value, facilitating conditions and hedonic motivation* significantly predicted behavioural intentions with an R2 value equal to 72%. The UTAUT2 model supported the role of performance expectancy and social commerce constructs in predicting perceived value and the role of effort expectancy and habit in predicting hedonic motivation [22].

#### *2.1.4 The technology, organisation and environment framework*

The Technology, Organisation and Environment (TOE) framework suggested by Tornatzky and Fleischer [23] states that the process of technological innovations in organisations is influenced by three dimensions, namely: the organisation context, the technological context and the external task environment (industry). They thus argue that for any organisation to adopt and implement technological innovations, the decision-making process involves consideration of these three areas. The application of TOE in the SMEs environment has been applied in several studies in SME community. The TOE framework was applied by Qalati and Anwar [24] in Pakistan among SMEs in the utilisation of social media.

#### *2.1.5 The task-technology fit theory*

The Task-Technology Fit (TTF) theory was developed by Goodhue and Thompson [25] to explain aspects of information systems and the persons who use it. These are the utilisation of technology, the technology itself and the individual using the technology [25]. In the application of TTF that relates closer to the WSMA and the eCommerce SME environment, Aljukhadar et al. [26] applied it to examine the drivers and consequences of *successful task completion* by a user in an online context. The theory suggests that the fit between characteristics of the task and those of the website predicts user performance and behavioural intentions [26]. The hypotheses developed were tested using the input of two large-scale studies performed in 12 industries and involving 13,135 participants [26]. Their results, which were replicated in a proximate culture, provided support to the predictions of Task-Technology Fit theory. It is further reported that the site information quality and ease of use were the only technology factors that significantly drove the users to a successful completion of their information tasks, rather than the site's graphical attractiveness, interactivity, security and privacy factors [26]. The findings further suggested that focusing on the enhancement of site characteristics that have low fit with the task is not effective as it resulted in slowing the successful completion of the online task [26].

Meanwhile, some theories in extant studies have been combined to improve their strength and to test their robustness especially in dynamic environments. Ghani et al., [19] combined TTF theory with the Theory of Planned Behaviour and found it to contribute to the most influential factor towards WSMA eCommerce adoption intentions. This implies that SMEs in developing countries such as Zambia can apply TTF and combine it where applicable in B2B eCommerce decision-making intentions for competitive advantage.

#### *2.1.6 Diffusion of innovation theory*

The Diffusion of Innovation Theory (DIT), proposed by Rogers [27], is one of the key theories of adoption and diffusion in the field of information systems. DIT states the following:


*Adoption of Web 2.0 Social Media eCommerce in SMEs: Conceptualising Theories and Factors… DOI: http://dx.doi.org/10.5772/intechopen.109604*

iii.Innovation is 'an idea, practice or object that is perceived as new by an individual or other unit of adoption [27]'

According to DIT, an innovation will be communicated over time through channels of communication within a particular social system [27]. Individuals are seen as possessing different degrees of willingness to adopt innovations, and thus, it is generally observed that the portion of the population adopting an innovation is approximately normally distributed over time along an S-shaped curve [27].

Parker and Castleman [28] argue that DIT when applied with all its constructs has better explanatory power because it includes a component of social dimension of SMEs rather than a collection of mitigating barriers and drivers.

#### *2.1.7 The perceived eReadiness model*

The Perceived e-Readiness Model (PERM) was developed by Molla & Licker [29, 30] specifically for developing countries context. The model considers some internal organisational factors, known as perceived organisational e-Readiness (POER), and external factors, identified as perceived external e-Readiness (PEER), as important for e-commerce adoption. The authors define POER to comprise the following:


PEER represents an organisation's assessment and evaluation of relevant external environmental factors (environmental imperative attributes) such as Government e-Readiness, Market Forces e-Readiness and Support Industries e-Readiness [29, 30]. The authors further claim that PERM can assist organisations in developing countries to locate, measure and manage risks in e-commerce adoption activities. Despite the goodwill for PERM, there is little evidence to support the application for this model in Web 2.0 eCommerce SMEs context of developing countries. According to [28], PERM does not recognise the influence of individual factors in e-commerce adoption although it emphasises organisational characteristics as being critical to the advancement of e-commerce in the organisation. It is noted that PERM is unable to capture small firm characteristics [28] which may be critical for social commerce consideration.

#### *2.1.8 The EBusiness satisfaction model*

The eCommerce business satisfaction model (EBS) [31, 32] was proposed to evaluate e-commerce success among SMEs from a business perspective. It is proposed that an EBS management model with 15 CSFs as a foundation was developed to assist SMEs' business managers in effectively adopting e-commerce systems or evaluating e-commerce success, which was categorised into five components including Marketing, Management Support and Customer Acceptance, Website Effectiveness and Cost, Managing Change and Knowledge and Skills [31, 32]. The EBS model

has well-defined organisational and eCommerce system structures as indicated in the critical success factor (CSF) [31, 32]. It's been reported to behave very well in Australia and China [31, 32], and it is yet to be tested in other regions, for example, Southern African countries such as Zambia. A drawback on the model is that it is unable to address certain characteristics of startup SMEs that do not have welldefined structures. Furthermore, the social media characteristics may not be fully embraced in the EBS model.

With respect to Trust, the EBS model [31, 32] defines it as 'trust in the interface design and information displayed on a website' which might be well suited for firms that are steps ahead in the eCommerce process. Hence, it might not capture the full spectrum of issues impacting SMEs in a developing country context, even as social commerce may just be in the foundation phase. To strengthen the application of EBS model in SMEs of developing countries, it might be useful to consider the whole spectrum of the social media application in use, in addition to the website design features.
