IT adoption within SMEs

Adoption of innovation is a complex phenomenon. Attempts have been made to explain it from three different perspectives: individual, structural, and interactive process (Slappendel 1996). We believe that Slappendels’s framework is the most appropriate framework to approach IT adoption by Indonesian SMEs. From each perspective, numerous studies have been made to explore the adoption of innovation. Research in the individualist framework tends to focus on the acts of the individual who initiates the adoption process, while the structuralists believe adoption of innovation is determined by the organisation’s characteristics and its environment. The interactive process school believes that adoption of innovation is a result of interactions between individuals, the organisation, and the environment. The views of the different perspectives are supported by the contingency theory of organisational behaviour, which recognises that an organisation is situated in an environment and consists of individuals who interact with each other within groups.

The intention of each individual within SMEs to use IT can be seen as resulting from efforts to spread the IT (diffusion) by other parties (Rogers 1995). For Indonesian SMEs’, the idea of using IT has been introduced by vendors and international customers (Schiller & Martin-Schiller 1997), while the government indirectly stimulates improvement in general business areas (Berry, Rodriguez & Sandee 2001). Meanwhile, as suggested by Cragg and King (1993) and Fink (1998), trading partners also contributed toward SMEs awareness of new technology.

At the individual level, as suggested by Davis (1989) through TAM, decisions to accept new technology were determined by individual perceptions of ease of use and usefulness. As described by Karahanna and Straub (1999) perception of ease of use was influenced by training and support availability and accessibility. In Indonesian SMEs’ case, this is where vendors of IT products take part. Vendors are an important way of opening access to new technology (Berry, Rodriguez & Sandee 2001). They may also provide the products and training to use them. Utomo and Dodgson (2001) suggested that educational institutions and governments are also able to provide such training.

Usefulness is influenced by availability of training and support, the social presence of the technology through communication channels, and social influence to use the technology (Karahanna & Straub 1999). Availability of training and support and awareness of technology have been covered by vendors and also government initiatives. Social influence to use the technology could come from business partners and customers, who often force Indonesian SMEs to use certain types of technology.

In conclusion, both IDPM (Rogers 1995) and TAM (Davis 1989) have been able to provide theoretical foundation for analysing individual adoption of IT. However, as Slappendel (1996) pointed out, adoption of innovation is not only an individual decision but also involves other elements within the individual environment (e.g. organisations). To some extent, IDPM and TAM have already shown that individual decisions were influenced by the environment. SMEs as organisations adopting IT have gone through interactions between individuals within the organisation and between the organisation and its environment. Therefore such interaction is covered in the IDPM and TAM. In the next section, we will discuss the factors influencing the adoption of IT.

Factors influencing IT adoption

The existing literature has documented some of the factors that affect IT adoption by SMEs in developed[1] countries (e.g. Chau 1995; Cragg & King 1993; Drew 2003; Dutta & Evrard 1999; Fink 1998; Ihlstrom et al. 2003; Thong & Yap 1995; Walczuch, Braven & Lundgren 2000). Meanwhile Al-Gahtani (2003), Khalfan and Alshawaf (2004), and Tarafdar and Vaidya (2005) focused on IT adoption in developing or less developed countries such as Saudi Arabia, Oman, and India. However, their studies were not specifically aimed at SMEs. So far only Utomo and Dodgson (2001) focused on SMEs in a developing country, Indonesia. However, their study only covered factors that influenced IT diffusion within Indonesian SMEs. Utomo and Dodgson’s (2001) study explored the diffusion of IT within Indonesian SMEs. They used a variant of case study method called the positivist case study approach (Leedy & Ormrod 2005; Yin 2003). They found factors influencing the diffusion of IT within Indonesian SMEs by analysing survey questionnaires. We did not find how those factors influenced IT adoption during the IT adoption process. It appears that those factors work individually as suggested by Slappendel (1996), as attributes of the individualist perspective. Utomo and Dodgson’s (2001) approach tends to look from the diffusion perspective. They were looking at how IT as innovation was being diffused to the general population (Indonesian SMEs) rather than how it was introduced, deployed, and used within an organisation.

We also noted that many of the previous studies have proposed drivers for and barriers to IT adoption. Drivers are factors that have positive impacts toward IT adoption and barriers are factors that have negative impacts toward IT adoption (Chau 2001; Ihlstrom et al. 2003; Lawrence 2002; Lee & Runge 2001; Rotchanakitumnuai & Speece 2003; Walczuch, Braven & Lundgren 2000). We use the term factors in this article instead of drivers and barriers. We argue that drivers and barriers are similar to factors with either negative or positive impacts toward IT adoption. For example, one barrier to IT adoption is unfamiliarity with the Internet (Walczuch, Braven & Lundgren 2000 p. 563). If the SME is familiar with the Internet it would not be a barrier but  a driver.

We classify the factors into those internal to the SME and those external, as suggested by Drew (2003) and Ilhstrom et.al.(2003). Internal factors are usually controllable by the SME’s management while external factors are less controllable or even uncontrollable by SME’s management. These factors and the research that identified them are shown in Table 1 and summarised in Figure 1 as part of the interactive process model. These factors are mostly derived from research in developed countries, except for Utomo and Dodgson (Indonesia).

Factors affecting SMEs’ IT adoption (from literature)

An interactive process-based model of IT adoption within SMEs

In light of such complexity, a combination of perspectives is needed to give a more comprehensive view of adoption of innovation. We used Slappendel’s framework (Slappendel 1996) as the basis of our analysis of research literature on adoption of innovation. In Slappendel’s original framework (Slappendel 1996) we did not find a specific model which could be used to explore and explain IT adoption within Indonesian SMEs. What we found was that Slappendel classified the theory of adoption of innovation into three categories. We believed that an interactive process model is the most appropriate approach to be used in this article. However, we did not find a model of such an interactive process in Slappendel’s framework. Slappendel’s framework only provides a perspective to look at adoption of innovation as an interaction between different stakeholders. Therefore we need to build an initial model that could guide us in exploring the adoption of IT by Indonesian SMEs from the interactive process perspective.

On the other hand we also have factors influencing IT adoption drawn from the research literature, as shown in Table 1. These factors are mostly drawn from studies using either individualist or structuralist perspectives. What we proposed next is the combination of factors influencing IT adoption within SMEs and how those factors interact during the process of IT adoption. Our proposed model has the following assumptions:

  • As each organisation consists of individuals who interact with each other (Robbins 2003), therefore it is necessary to acknowledge that individual characteristics and their actions influence the adoption of innovation.
  • Organisations are situated and interact within their environment (Robbins 2003; Zaltman, Duncan & Holbek 1973), therefore it is also important to acknowledge that environment and organisational characteristics influence the adoption of innovation.

Relevant literature for the adoption of IT within SMEs has been searched and analysed. It was used to define and understand the different nature and ideas about IT adoption within SMEs (Hart 1998; Leedy & Ormrod 2005). This review included the following disciplines:

  • Adoption of innovation, particularly related to IT adoption. Since IT is a wide discipline, the literature review also covered areas such as software engineering and information systems.
  • Management, especially related to managing SMEs.
  • SMEs and entrepreneurship.
  • Organisational behaviour.

Therefore in viewing the process it is necessary to include individuals, organisational characteristics, and the environment (competitors, customers, trading partners, and government), as depicted in Figure 1.

Multi perspectives on innovation on organisation

Government interaction with the organisation is not shown as a solid line since it was not clearly documented within the literature (Slappendel 1996). Based on this model, the adoption of innovation is shown as a process where:

  • Individuals, organisation resources and characteristics are involved and interact in order to adopt innovation. In this model, the individuals are the manager and staff, while other resources are organisational resources that are used and managed by individuals.
  • Customers, trading partners, and competitors may influence the adoption of innovation, while the government can drive the adoption of innovation indirectly by implementing policy and providing stimuli, although this is unclear.
IT adoption process

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[1] Worldbank classifies and lists developed, developing, and less developed countries based on their Human Development Index (HDI), where a developed country has HDI 0.8 or better, developing countries have HDIs between 0.5 and 0.8, and less developed countries have HDIs less than 0.5.

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