Determining the use of E-business in European firms in light of the TOE-framework

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Determining the use of E-business in European firms in

light of the TOE-framework

Master thesis, MSc BA, specialisation International Business Management University of Groningen, Faculty of Economics and Business

June 12th, 2015

BARBARA SOLANGE BROMMER

Student number: 1781855 Hoofdweg 246 9695AV Bellingwolde Tel.: +31 (0)630894472 e-mail: bsbrommer@gmail.com Supervisor: Dr. R.W. de Vries Co-assessor: Drs. J. van Polen Acknowledgement:

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Abstract

E-business use is found to be an imperative for creating value in doing business today.

Therefore it is highly relevant to increase our understanding of the factors determining the use of E-business by firms. To gain more insights into the internal and external factors

determining E-business use, this study is grounded in the

Technology-Organization-Environment (TOE) framework, and the resource dependence theory (RDT). Data from the E-business W@tch 2005 is used, containing 5218 cases from 7 European countries. The

hypotheses are tested using multiple regression analyses, while controlling for country differences. The findings partially support the proposed hypotheses. This research delivers a contribution to our current knowledge on the factors determining the use of E-business in firms. Furthermore, it confirms the usefulness of the TOE-framework for studying the use of E-business by firms, and also suggests the RDT as a useful extension of the TOE-framework.

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1. Introduction

Over the last decade a huge growth in business that is performed electronically is apparent, also referred to as E-business. Ever since the development of E-business in the 1960´s, it is still as relevant as ever (Bjørn-Andersen & Raymond, 2014). Companies face the important choice of whether or not to use E-business and if so to what extent. Implementing E-business is a strategic choice that concerns and influences the entire business strategy of a firm (Porter, 2001). However, if successful, firms can greatly maximize business value and increase

performance (Zhu, Kraemer & Xu 2006). Since implementing E-business may completely change business models and organizational structures, as well as the relationships with business partners (Bordonaba-Juste, Lucia-Palacios & Polo-Redondo, 2012), companies and managers need to make well-informed decisions.

Many factors concerning E-business have been researched, like the adoption, the antecedents and the consequences of E-business (Bordonaba-Juste et al., 2012; Oliveira & Martins, 2010). As well as the influence of E-business on firms’ strategy (Damanpour & Damanpour, 2001), innovation (Euripidis & Fotini, 2012) and stakeholder relationships (Yee-Loong Chong, Ooi, Lin & Tang, 2009; Croom 2005; Ragins & Greco, 2003). However in current literature little is discussed about the elements that influence the use of E-business by a firm. The use of E-business in a firm is influenced by internal factors (Li & Xie, 2012; Zhu & Kraemer, 2002), as well as external factors (Damanpour & Damanpour, 2001; Dwivedi et al., 2011) This research will fill this gap by examining the internal and external elements that determine the possibilities of successful use of E-business for a firm.

Such internal factors include, for instance, whether the firm has IT skilled employees, how developed the firm’s technology is, etc. (Davila, Gupta & Palmer, 2003; Venkatesh & Speier, 2000). External factors include more interactive factors, such as the relationship of the firm with its customers, suppliers and the government (Gibbs & Kraemer, 2004). These relationships are more complex as they concern interdependencies between the firm and its business partners, over which the firm does not have full control (Drees & Heugens, 2013). However there is a need to understand the influence of these relationships between the firm and its environment on the use on E-business (Wu, Mahajan & Balasubramanian, 2003).

In order to determine the influence of factors that influence the use of E-business of the firm, this study uses the TOE-framework, which discusses the Technological,

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framework to gain more insight into the complex adoption processes of technological innovations. It is now extensively used by authors (Baker, 2011).

The technological context is concerned with the technological equipment, systems and processes both internal and external to the firm (Zhu & Kraemer, 2005). The organizational context refers to characteristics of the firm itself (Tornatzky & Fleischer, 1990). Finally, the environmental context addresses the conditions under which the firm is operating (Lucia-Palacios, Bordonaba-Juste, Polo-Redondo & Grunhagen, 2014). The environmental context can be unpredictable and dynamic, due to the interactions with other parties that occur (Chau & Tam, 1997). Therefore we extent the TOE-framework with the Resource Dependence Theory (RDT), in order to increase our understanding of the influence of environmental element on the use of E-business by the firm. The RDT is since its development in 1978 (Pfeffer & Salancik, 1978), extensively used to explain organizational- environmental relationships (Drees & Heugens, 2013).

The aim of this study is to increase our current knowledge of the elements that influence the use of E-business. It does so by empirically testing the influence of the TOE-elements on the use of E-business within a firm. Hereby this study also determines the usefulness of the TOE-framework as a grounding theory for measuring the use of E-business in a firm. Furthermore, to increase our understanding of how the interactions between the firm and its environmental elements influence the firm’s use of E-business, this research extents the TOE-framework with the resource dependence theory. Lastly, this study controls for country differences as previous research has indicated that the level of E-business

development can differ per country (Bordonaba-Juste et al., 2012), which might influence the use of E-business in a country. The outcome of this study can be a guideline for future

research to build on for researching the use of E-business by firms.

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The remainder of this paper is structured as follows. The next chapter introduces the theoretical framework used in this research. It discusses the existing knowledge on E-business literature and E-business use in relation to the TOE-framework. The third chapter gives an overview of the data collection process, the variables and the methodology. This is followed by the fourth chapter which outlines the results of the statistical research. The fifth chapter provides a discussion of the results, followed by limitations, implications and suggestions for future research. The final chapter provides a short concluding summary of this research.

2. Theoretical overview

2.1 Adopting E-business

Ever since the early days of the Internet researchers have discussed the opportunities of the innovating Internet technologies and the business possibilities and implications for companies (Bakos, 1998). At the present time the implications and opportunities of E-business and predictions of future possibilities with this new technology are researched frequently (Bjørn-Andersen & Raymond, 2014; Weber & Kauffman, 2011). However, where researchers initially focused on the advantages that the Internet technologies could bring for firms and whether such innovations should be adopted or not, they now claim that E-business is an imperative for all companies to do business (Damanpour & Damanpour 2001; Porter, 2001). As doing business is ultimately about increasing value for a company, firms will want to adopt E-business to positively influence business value (Lin, 2008). The decision is no longer whether to adopt an E-business strategy, but to what extent. This study aims to provide better insights into the factors that determine the level of use of E-business by a firm..

This research argues that E-business can be found in all aspects of a business and influences both internal and external processes of a company (Lin, 2008). Implementing E-business in a firm can simply refer to creating a website and an e-mail system, to integrating the companies’ systems with the value chain both back- and forward (Avlonitits & Karayanni, 2000). To include all these aspects in the definition of E-business used, the definition is based on definitions provided by Zhu, Kraemer & Xu (2006) and Croom (2005): E-business refers

to using Internet-based electronic business (E-business) for information exchange,

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6 2.1.1. The benefits of using E-business

The factors surrounding the choice of companies to either use or not use E-business have been widely discussed (Weber & Kauffman, 2011; Li & Xie, 2012). Important for understanding why firms use E-business is to understand the advantages it provides for them. These advantages are not just determined by the external and internal factors surrounding the firm, but also by the motivation of firms to use E-business. Companies are always looking to increase the business value and in literature it is found that the use of E-business can have a significant influence on maximizing business value (Lin, 2008) and firm performance (Zhu & Kraemer, 2005; Zhu, Kraemer & Xu 2006). A prerequisite is that the E-business is included in the total business strategy although the main focus should remain on a core strategic approach that makes the company distinct from its competitors (Lederer, Mirchandani & Sims, 2001; Porter, 2001). As stated in the introduction, E-business is an imperative according to Porter (2001). However he does argue that for giving real value to its customers, a company should integrate its E-business strategy in its overall strategy. He argues that true competitive advantage will still come from ‘unique products, proprietary content, distinctive physical activities, superior product knowledge, and strong personal service and relationships’ (Porter 2001, p. 17). It is this competitive advantage that will lead to business value for the company. In addition, Damanpour & Damanpour (2001) discuss six possibilities and benefits of using E-business, which are better information management, better supply chain integration, better partnership in the channel, lower transaction costs and better geographical coverage.

Jahanshahi, Zhang & Brem (2013) point out that further gains can be made by more just-in-time production allowing overhead costs to be reduced and inventory to be smaller. This saves costs through reduced processing times. In addition, E-business allows internal processes to be used more effectively and efficiently, which leads to added value (Zhu & Kraemer, 2002), of which better customer services are an example. The benefits as presented above, show that there are many ways in which E-business can contribute to value creation for a firm. These are also the main reasons why it is attractive for companies to use E-business.

2.2 TOE-framework elements in relation to E-business

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developed by Tornatzky & Fleischer (1990), and originates from the research field of

organizational psychology. It discusses companies’ choice for adopting new technology based on three elements. The TOE-framework stands for the Technological, Organizational and Environmental context that a firm operates in, and can be used to research the intention to adopt, the need to adopt and the likelihood of adopting new technological innovations. It is developed by Tornatzky & Fleischer (1990) to analyse the context in which companies adopt technological innovations like E-business (Chau & Tam, 1997; Raymond, Bergeron & Blili, 2005; Zhu et al., 2006; Zhu, Xu & Dedrick, 2003). Or as Baker (2011) provides in his chapter on the TOE-framework: “Extant research has demonstrated that the TOE model has broad

applicability and possesses explanatory power across a number of technological, industrial, and national/cultural contexts” (p. 235).

Together the three context elements influence the ‘technological innovation decision making’ of a firm. Over the past two decades the TOE-framework has been used by several authors in relation to the adoption of innovative technology and E-business practices (Kuan & Chau, 2001; Oliveira & Martins, 2010; Premkumar & Ramamurthy, 1995; Ramdani, Kawalek & Lorenzo, 2009). It was first applied to E-business by Zhu et al. (2003), who confirm the applicability of the TOE-framework for their studies on electronic business adoption by European firms, and continue to use it in their further studies on the topic, performed both for the developed world regions as well as the developing world regions (Zhu & Kraemer, 2005; Zhu et al., 2006).

When the context is favourably developed for E-business, firms are more likely to use E-business (Kraemer, Gibbs & Dedrick, 2002). This is supported by Molla & Licker (2005) who find that the lack of such supportive factors will hinder the use of E-business. Based on the TOE-framework, we will discuss the factors that influence the extent to which a firm uses E-business below. At the end of this chapter, figure 1 provides an overview of the relationship between the elements of the TOE-framework and the ‘use of E-business’, as researched in this study.

2.2.1. Technological element

The technological context refers to all available technological processes both external and internal to the company, such as technological equipment and technological systems

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advantage that a company can have relative to its competitors. By implementing innovative technology a firm can create a first-mover advantage compared to its competitors

(Varadarajan, Yadav & Shankar, 2008). However innovative technology brings organizational changes of which the impact on the firm should be managed (Dwivedi et al., 2011).

Use of technological processes

In this study technological processes refer to the Internet-related technological systems that can be implemented for Internet-related business, like electronic resource planning (ERP) systems, customer relationship management (CRM) software systems, Intranet, Extranet etc. (E-business W@tch, 2007). Such processes enable suppliers, business partners, customers and employees to be linked together through the use of Internet-related technology (Wu et al., 2003). Technological processes increase the flow of information between the firm and

stakeholders, and makes more timely and precise information exchanges possible (Oliveira & Martins, 2010). The more advanced the existing technological system of the firm is, the more likely it is that both the firm and the existing technology are ready for further E-business implementation (Davila et al., 2003; Zhu & Kraemer, 2005). When firms are already familiar with IT processes they are more likely to realize the potential benefit of such systems, and will more easily expand their use of E-business. Therefore, we expect that firms that have more technological processes in place are more likely to invest in other technological processes for E-business. This expectation leads to the following hypothesis.

H1a:A higher level of use of technological processes is positively related to perceived use of E-business.

Technological innovation

As mentioned above, in the context of the use of E-business, companies are interested in the added value that can be created. In the technological element value creation can take place by effectively increasing technological capabilities (Zhu, 2004). Such capabilities are increased by implementing new technological processes, which create or further expand Internet-related technological business links between the firms and their suppliers, business partners,

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likely to extend their use of E-business, and adopt further new technologies. This leads to the following hypothesis.

H1b: Higher adoption of technological innovations is positively related to perceived use of E-business.

2.2.2. Organizational element.

The organizational context encompasses the characteristics and resources of the company, like firm size, managerial structure, number of researchers etc. (Baker, 2011). Most frequently discussed in the organizational context are resources (slack) and firm size, although Tornatzky and Fleischer (1990), argue that resources (slack) are “neither necessary nor sufficient for

innovation to occur” (p. 161). Firm size is also widely discussed although the significance of

firm size remains unclear. We will discuss the organizational elements below. Firm size

One of the organizational elements that influences a firm’s decision about whether or not to adopt E-business is firm size (Zhu & Kraemer, 2005). Large firms often have more resources available to them to implement new innovations (Tornatzky & Fleischer, 1990), and are probably more effective in innovation implementation ( Li & Vanhaverbeke, 2009). However, they also suffer from structural inertia, which makes it harder for them to follow through the necessary business changes in order to successfully implement E-business and which can negatively influence the value added from E-business adoption (Thong, 1999). Furthermore, Chen & Hambrick (1995) address the fact that larger companies are less flexible than small firms. Large companies often have a more extended and more invested relationship with their suppliers, which makes the effort of switching suppliers more difficult (Gallaugher, 1997). Small firms, on the other hand, are more flexible as they require less communication and can coordinate E-business implementation more efficiently ( Zhu & Kraemer, 2005). Li & Xie (2012) additionally find that small firms are more likely to use the implementation of E-business to compete with large firms. As small firms are more flexible and cope better with the implementation of E-business, we expect a smaller firm size to lead to a higher perceived use of E-business.

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10 Firm scope

Firm scope refers to the international markets in which a company operates. Considering the international span of this research, it is important to include firm scope. The Internet allows for easier access into international markets. Therefore the markets in which a firm operates can be more geographically spread (Zhu & Kraemer, 2005). Using E-business, the value chain can be internationally coordinated very effectively (Porter & Millar, 1985). This allows firms to choose the most efficient and beneficial supplier, even if they are situated in another country. It further allows firms to have an international customer base. Research has already shown that there is a positive relationship between globalization and e-commerce adoption (Jaw & Chen, 2006; Gregory, Karavdic & Zou, 2007). Thus, E-business allows for efficient coordination along the value chain, which is most necessary for firms with an international scope. Moreover, in their study Zhu et al. (2003) found that firm scope is a strong driver for E-business adoption, which leads to E-business value. A wider customer base and a bigger market to sell in provide the company with an opportunity for a larger turnover. To assure good coordination along a broader geographic scope, E-business provides a good solution. Therefore we expect the following hypothesis.

H2b: A geographically broader firm scope is positively related to perceived use of E-business.

IT expertise investment

When a firm decides to adopt E-business, this does not simply entail implementing the

required technical software. It requires the hiring of IT-skilled employees, training the current employees, changing the workflow etc. (Falk, 2005). In order to implement E-business correctly, the right knowledge needs to be present within the firm, for instance to train

employees. Like Venkatesh & Speier (2000) indicate, in the process of technological adaption it is a necessity for the firm to have the availability of training and IT expertise. This is further supported by Lin & Lee (2005) who have found that firms with their own E-business

specialists are better capable of managing their value chain and are therefore more likely to use E-business. Thong (1999) finds that more IT knowledge leads to more confidence in adopting IT innovations. When IT expertise is present, firms are more aware of the

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use of E-business further. This leads to the expectation that a recent investment in IT expertise leads to a higher perceived use of E-business.

H2c: Firms that recruited IT professionals in the last 12 months have a higher perceived use of E-business.

2.2.3. Environmental element.

Lastly the environmental context describes the environmental conditions under which the company is operating, like the regulatory environment. This includes the absence or presence of technology service providers, the competition intensity of the industry, the structure and the size of the industry and the regulatory environment (Tornatzky and Fleischer, 1990). These conditions are not controlled by managers, as they include relationships with, and external pressures of, the direct environment on the company (Chau & Tam, 1997; Gibbs & Kraemer, 2004). A distinction can be made here between the influence of direct and indirect

environmental elements. Direct environmental elements are concerned with the operating environment specific to the firm. Indirect influences are competitors, customers and suppliers which affect the environmental conditions under which the firm operates, but whose actions are not directly targeted at the firm (Damanpour & Damanpour, 2001). In the context of E-business the relationship with external stakeholders is very important. E-E-business serves as a tool “to build and manage relationships with customers, suppliers, employees and partners”

(Lucia-Palacios et al., 2013, p.7.).

The focus of the TOE-framework is on explaining the adoption of technological innovations within the firm. Therefore it is sufficient in explaining the environmental factors that lead to the adoption of technological innovations. However, the TOE-framework does not provide an explanation for the relational dynamics within the environmental context.

Therefore, this research includes the Resource Dependence Theory (RDT) to better understand these relational dynamics that play a part in the adoption of technological innovations.

2.2.3.1 Resource dependence theory

The resource dependence theory is an important framework for explaining organizational-environmental relationships (Drees & Heugens, 2013). In their research “The external control

of organizations”, Pfeffer & Salancik (1978) have combined many of the previous existing

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the resource dependence theory, which seeks to explain why firms engage in interdependent relationships with other firms in their environment. The theory was widely accepted in the organizational and strategic literature (Hillman, Withers & Collins, 2009). The idea of RDT is that companies are not self-sufficient from their internal resources and therefore need

resources from their environment to gain all required resources (Phan, 2002). This makes them dependent of or interdependent with other organizations. Firms want to manage these (inter)dependencies, however they are not always capable to do so (Casciaro & Piskorski, 2004). For a simplified explanation of the industry interdependencies, as explained by Pfeffer & Salancik (1978), see box 1 in appendix A.1.

As can be seen from the explanation in box 1, interdependencies cause uncertainty as they allow other organizations to have an influence on future successes that the firm can achieve (Hillman et al., 2009). A need for autonomy for the firm is then triggered, in order to decrease this uncertainty and (inter)dependence. When the firm feels autonomous this restores the feeling of power over obtaining required resources and future successes (Ulrich & Barney, 1984). Thus, the dependence of resources is also a basis of power. The more critical a

resource is for the firm, the larger the power becomes that the provider of this resource holds over the firm.

Pfeffer & Salancik (1978) argue that firms want to diminish the uncertainty and the (inter)dependence that come from this resource dependence, and thus this power that other environmental stakeholders hold over the firm. To achieve this firms need to adjust their internal and external strategy. E-business is a useful tool in this process as it can play an important role in shaping firms’ internal and external strategy (Phan, 2002; Lucia-Palacios et al., 2013). Furthermore, E-business allows firms to develop better relationships with their suppliers, customers and other stakeholders, which provides more control over the

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13 Supplier pressure

In line with the RDT the relationship of the firm with its suppliers is very important as it is dependent on its supplier for their final product (Pfeffer & Salancik, 1978). Both companies are searching to maximize the supply chain efficiency, as this can lead to improved

performance (Kraemer et al., 2002). E-business provides a means to optimize the supply chain between the business partners, allowing for more efficient value creating processes (Sawhney & Zabin, 2001). However, this means that companies do not just deal with their own internal business or E-business alignment, but also have to consider business or E-business alignment with their supplier (Avlonitis & Karayanni, 2000; Finnegan, Galliers & Powell, 1999). When E-business is aligned, both parties can enjoy common knowledge and knowledge sharing even though they differ in their specialism (Li & Vanhaverbeke, 2009). However, as the RDT states, firms find themselves dependent on or interdependent with their supplier. This

dependence gives the supplier power over the firm (Pfeffer & Salancik, 1978). In the context of maximizing the supply chain to create more value, the choice of the supplier to use E-business will consequently place pressure on the firm itself to use E-E-business (Davila, Gupta & Palmer, 2003). Using E-business is interesting for suppliers to increase their supply chain efficiency in several ways. It allows for lower transportation costs as E-business allows the supplier to act more timely and more precisely to when and where the products are needed, which also leads to lower inventory costs (Bjorn-Andersen & Raymond, 2013; Porter & Millar, 1985). Moreover, suppliers already working with E-business will want their business partners to adopt such systems as well, so they can significantly increase efficiency and communication within the supply chain (Markus, 2000). This in turn can lead to business value maximization for example through better quality consistency (Croom, 2005; Lin, 2008). In sum, an efficient supply chain can lead to cost savings by improving communication, leading to more timely information, a better response time of the supplier and less inventory being necessary, with greater flexibility (Damanpour & Damanpour, 2001; Yee-Loong Chong et al., 2009). Based on the RDT we find that suppliers have specific interdependencies with firms and are expected to hold a certain power over them. Suppliers will want to maximize the supply chain value through which they provide necessary resources. In this combination we expect that supplier pressure can significantly influence the decision for firms to use E-business and therefore leads to a higher perceived use of E-E-business.

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14 Customer pressure

Concerning customer pressure, in their research Avlonitis & Karayanni (2000) find that information exchange is facilitated by the Internet and leads to new customer needs. In line with the RDT, companies want customers to buy their products or services, as the sale of their products provides a firm with profit (Pfeffer & Salancik, 1978; Porter, 2001). This creates substantial power for the customer, which is discussed by Wu et al. (2003), as this provides customers with leverage to pressure firms into adopting new technologies. This is also addressed by the RDT, which states that customer demand is the ultimate resource, as it determines a firm’s success (Pfeffer & Salancik, 1978). E-business is interesting for customers as these new technologies may help companies to decrease customer loss and service costs and to increase sales (Ragins & Greco, 2003). An example of such technology are tracking systems where customers can see where their order is and when it will be

delivered. For customers this lowers their transactional risk (Zhu & Kraemer, 2005) and they can expect such service from a company before wanting to do business. Furthermore, E-business can facilitate administrative processes to be handled better and more efficiently, like accounting and invoicing (Wu et al., 2003). E-business can offer personalization, and can increase the access to companies’ products (Hammel & Sampler, 1998). Markus (2000) finds that adopting E-business is no longer a choice, as customers expect that a good E-business system is in place. When a customer finds a competitor that does provide the convenient service of a good E-business system, he can easily switch to this competitor. In sum, based on the reduction of costs and the increase in benefits that customers can experience from E-business, they may demand it from firms, otherwise switching to competitors who can provide the desired technology (Wu et al., 2003). According to the RDT the customer buying from the firm is the ultimate resource. Considering this, we expect the following hypothesis.

H3b: Higher perceived customer pressure leads to higher perceived use of E-business.

Competitive pressure

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extent that they can satisfy the customer better than competition can, as there is always the threat of customers leaving to the competitor (Markus, 2000). This is indirectly explained by the RDT, which finds that the customer is the ultimate resource, and when the customer leaves to the competitor this therefore poses a big threat to the company (Pfeffer & Salancik, 1978). Premkumar & Ramamurthy (1995) find that firms who face strong competition need to maintain updated information about their competitors, in order to make the best competitive decisions, for which technological innovations are found to be essential. Ranganathan,

Dhaliwal & Teo (2004) suggest that firms in highly competitive environments use E-business systems to strengthen integration with suppliers. By maximizing the supply chain efficiency, and thereby successfully managing supply chain interdependencies, E-business can provide a competitive edge (Phan, 2002; Scarborough and Spatarella, 1998). Lastly, the use of E-business allows firms to offer a specific type of value to their customers, by offering a unique strategy of services, features and logistical arrangements (Porter, 2001). To conclude, in order to be competitive, the value chain interdependencies should be managed successfully, and the value chain efficiency with both customers and suppliers should be maximized. E-business provides a means to gain such competitive advantages (Phan, 2002). Having a competitive edge allows a firm to be more successful than its competitors (Porter, 2001). We expect that under higher competitive pressure, firms feel that they have to respond by using E-business. Therefore, in a more competitive environment a higher perceived use of E-business is expected. The following hypothesis is proposed.

H3c:Higher perceived competitive pressure leads to higher perceived use of E-business.

Regulatory environment

The regulatory environment is found to be important in influencing E-business adoption (Kraemer, Gibbs & Dedrick, 2002). The open system of the Internet requires regulations by the government to make the system reliable. Such regulations include business and tax laws that support E-business usage and that create an appropriate institutional framework (Sato, Hawkins, & Berentsen, 2001). The RDT finds the regulatory environment to be a source of power that can decide and control the use of resources, which makes the regulatory

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dependency and uncertainty on other firms (Hillman et al., 2009) In their research on E-commerce Li & Xie (2012) found that government policies are an important factor in determining the adoption of E-commerce: “the efficiency of the legal system and proactive

government policy, influences firms’ decision a lot” (P.7.). The government can create a

stimulating regulatory environment for E-business use by, for example, creating incentives for the use of E-business. Finally, Gibbs & Kraemer (2004) find that a stable regulatory

environment can increase confidence and trust in E-business and can decrease the

uncertainties that firms feel towards it. Therefore, I expect that a more supportive regulatory environment increases the perceived use of E-business, leading to the following hypothesis:

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Legend:

Factors

Control variables

TECHNOLOGICAL CONTEXT:

- Level of use of technological processes - Technological innovation - ORGANIZATIONAL CONTEXT: - Firm size - Firm scope - IT expertise ENVIRONMENTAL CONTEXT: - Supplier pressure - Customer pressure - Competitive pressure - Regulatory environment Use of E-business CONROL VARIABLES: - Firm sector - GDP per capita ($)

- Ease of doing business

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3. Methods

This research uses the European W@tch database, which was launched in late 2001 by the European Commission as an instrument to monitor the development, adoption and impact of E-business across different sectors of the economy in the countries of the European Union and EEA countries. The goal of the initiative is to stimulate E-business adoption and to increase the competitiveness of European companies. This database has been used by multiple official institutions, such as the OECD (2004) and the European Commission. It has also been used in earlier studies concerning E-business, e.g. Falk (2005) used the 2002 survey, Koellinger (2008) used the 2003 database and Bordonaba-Juste et al. (2012) used the 2007 database. The E-business survey of 2005 was the third survey, after previous surveys in 2002 and 2003. The E-business survey of 2005 is especially aimed at “the use of ICT systems to support

e-procurement and online sales processes “(The E-business Survey, Annex I: 2005, p.1).

Unfortunately, the website of E-business W@tch has been archived since 2011 http://ec.europa.eu/enterprise/archives/e-business-watch/). Therefore, it is not possible to request the data from the website itself anymore. To overcome this we approached the company that has performed and managed the research from 2002-2009 and owns the archived website which is the company Empirica GmbH. After extended contact they were still unsure whether they could provide the requested data from their archive. In this period of time we contacted several authors who had previously used the data and finally the author Dr. Falk (2005) was able, in consensus with Empirica GmbH, to provide the 2005 dataset. 3.1 Sample and procedure

For the survey of 2005 solely computer-using companies were considered. This was done as was found from previous surveys that 99% or more of all medium and large firms considered use computers. Only for micro and small firms a small difference might occur as in some sectors a percentage of around 10% do not use computers, mainly in the food and beverages, textile, construction and tourism sector. Accordingly a small difference should be expected when representing ‘all firms’. The survey consisted of 5218 telephone interviews that were carried out in January and February 2005. They were conducted based on the CATI

(computer-aided telephone interview) method via telephone. The interviewers were instructed to interview the person in charge of IT-related decisions. This could be a person in

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Only a limited number of European countries were included in the data collection. The

following seven countries were included; France, Germany, Italy, Spain, UK, Czech Republic and Poland (see Appendix B.1). The telephone interviews were evenly spread over the

included countries. In each country, companies were approached that were nationally active. Regarding the sectors that were included, there were ten sectors considered based on the NACE Rev 1.1 categories (The E-business Survey, Annex I: 2005). See Appendix B.2 for an overview of the industries.

The data was collected by drawing a random sample, with set targets for successful telephone interviews per country per sector. For almost all country-sectors a target of 80 successful telephone interviews was reached, with the big exception of the Aerospace

industry. For this industry a minimum of 3 successful telephone interviews was conducted in Poland, and a maximum of 39 successful telephone interviews in France.

3.2 Measures

3.2.1 Dependent variable

Perceived use of E-business: The dependent variable is measured based on the survey question “Would you say that e-business constitutes a significant part of the way your company operates today, or some part or none at all?” (Bordonaba-Juste et al., 2012). The scale was negative and had to be rescaled to a positive scale ( 1 = none at all, 2 = some part, 3 = significant part). When the answer ‘don’t know’ was given it was treated as a missing variable.

3.2.2 Independent variables Technological elements:

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replied with ‘yes’ depicts the level of use of technological processes (ranging from 1 to 8, where 8 is the highest level of use of technological processes).

Adoption of technological innovations: Two survey questions measure the level of adoption of technological innovations (Euripidis & Fotini, 2012; Koellinger, 2008). The first question is related to the introduction of product or services innovations and the second question is related to the introduction of process innovations. These questions could be answered with ‘yes’ and ‘no’, of which a dummy variable was created for both questions (0 = no, 1 = yes). As a next step both variables were summed to create a level of adoption of technological innovations.

Organizational elements:

Firm size: For determining firm size this study examines the number of employees of the firm (Zhu et al., 2006). Within the dataset stratified sampling was used per country-sector based on firm size, to ensure that all sizes are included. This categorization is based on the EU

classification by numbers of employees (OECD, 2005), 35% share of micro enterprises (<10 employees), 25% share of small enterprises (10-49 employees), 30% share of medium sized enterprises (50-249 employees) and 10% share of large companies (250+ employees) (The E-business Survey, Annex I: 2005).

Geographical firm scope: By measuring the company’s ‘most significant market’, the geographical broadness of firm scope is determined. The survey question could be answered with ‘regional market’, ‘national market’ or ‘international market’. This variable is used as a ranking variable where regional market = 1, national market = 2 and international market = 3, accordingly a higher number indicates a higher level of geographical firm scope.

IT expertise: IT expertise is considered by Bordonaba-Juste et al. (2012), to measure the IT knowledge that is present within the firm. This study measures IT expertise by the following survey question: “ Has your company during the past 12 months recruited or tried to recruit

staff with special ICT or E-business skills?” The variable is treated as a dummy variable ( 0 =

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21 Environmental element:

Supplier pressure: The following survey question represents the supplier pressure

(Bordonaba-Juste et al., 2012): “How important was this reason why your company decided to engage in E-business activities?” – Because your suppliers expected it from you. As the answers were ranked negatively on a scale from 1 to 4, from very important to not important at all, the scale was reversed (1 = not important at all, 2 = rather unimportant, 3 = rather important, 4 = very important).

Customer pressure: Following Bordonaba-Juste et al. (2012), customer pressure is measured as follows: “How important was this reason why your company decided to engage in E-business activities?” – Because your customers expected it from you. This scale was also reversed as performed for supplier pressure.

Competitive pressure: This variable measures the amount of pressure that a firm experiences due to circumstances in the competitive environment. One survey question depicts the competitive pressure from the environment:“How important was this reason why your

company decided to engage in E-business activities?” – Because your competitors also engage in E-business. The scale of the survey question was negatively scaled from ‘very

important’ to ‘not important at all’. For measurement this scale was reversed.

Perceived regulatory environment: The perceived regulatory environment reflects the regulatory environment as viewed by the firm and therefore does not by definition reflect the actual regulatory environment that the firm is in. The perceived regulatory environment is measured based on 5 survey questions that determine the legal problems and complications that the firm faced. This measurement reflects the somewhat subjective view of the manager on when legal problems or complications are faced, and the extent to which he is aware of them. An example of a question is “When doing business electronically, has your company

actually faced any legal problems or complications - when participating in electronic marketplaces or other trading platforms?” Each question could be answered with ‘yes’ or

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regulatory environment. An overview of the survey questions representing the regulatory environment can be found in appendix B.4.

3.2.3 Control variables

Firm sector: Among industries there might be differences regarding the use of innovative practices as found by De Jong & Vermeulen (2006) in their research among small firms in 7 different industries. Furthermore, Falk (2005) found differences in the use of E-business technology between industries. Zhu et al. (2003) propose that it is common in the information systems research to consider industry as a control variable. Therefore it is important to control for the possibility of industry differences. See appendix B.2 for an overview of the sectors.

Country differences:

In this cross-country research it is important to control for country differences as previous research indicates differences between countries in the adoption and use of E-business (Koellinger, 2008; Oliveira & Martins, 2010). Research by Zhu et al. (2003) measures that there are differences between countries with high E-business intensity and countries with low E-business intensity. Likewise, Bordonaba-Juste et al. (2012) confirm such country

differences based on the level of e-commerce of the country.

There are two ways in which this study controls for country differences. First, by controlling for country differences in the perceived use of E-business by performing a one way ANOVA (F-test). Second, by including the Gross Domestic Product (GDP) per capita of 2005 per country and a business environment ranking for each country as a control variable when performing the regression analyses.

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The GDP per capita is calculated by dividing the gross domestic product by the midyear population. It is expressed in constant 2005 US dollars. The United Kingdom has the highest GDP per capita with $ 39.934,90, and Poland has the lowest GDP per capita with $ 7.976,10. The mean of all country GDP’s per capita is $ 26.933,68. An overview can be found in appendix B.5.

Ease of doing business: There are differences between countries in the circumstances of doing business that influence the ease of using E-business in a country. For the

implementation of E-business financial resources are necessary (Gibbs et al., 2003). When it is easier to get the necessary credit to invest in new technologies, the level of E-business use might be higher. Furthermore, it is important for E-business that this is reliable and that a good system is present, for example, to enforce contracts. Esty & Porter (2001) suggest that growing as a firm requires resources and that the ease of doing business can either place restraints on the growth of a firm or support growth. When this growth is aimed at E-business and the overall ease of doing business in a country is higher, it is also easier to implement E-business and therefore the level of E-E-business could be higher. This makes it important to control for ease of doing business.

For the business environment ranking the ‘ease of doing business’ index is used (www.doingbusiness.org), which is a project of The World Bank. This index measures the business regulations and the enforcements of these regulations for 189 economies. It is based on ten indicators, such as ‘getting credit’, ‘paying taxes’, ‘enforcing contracts’, ‘resolving insolvency’, etc. An overview of the ten indicators is provided in Appendix B.6. In this research the country ranking of 2006 is used, as this is based on data of January 2005, which means that this ranking represents the circumstances of doing business in 2005.

In the country rankings of the index, the highest ranking means that the environment provides more ‘ease’ in doing business in that country. In the ‘ease of doing business’ ranking the order is as follows, with the first country having the highest ‘ease of doing business’: United

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‘registering property’, ‘getting credit’ and ‘labour market flexibility’ (Ease-of-doing-business full report 2006), causing it to score relatively low on the general index. For measuring a linear regression we scale the ranking from ‘1 = country with lowest ease of doing business’ and ‘7 = country with highest ease of doing business. This gives the following ranking: 1 = Italy (lowest ‘ease of doing business’), 2 = Poland, 3 = France, 4 = Czech Republic, 5 = Spain, 6 = Germany and 7 = United Kingdom (highest ease of doing business).

4. Results

4.1 Descriptive statistics and correlations

Before running the regressions we first explore the data by looking at the descriptive statistics and correlations. Table 1 shows the descriptive statistics of the dependent and independent variables. The descriptive statistics for each type of variable were adjusted accordingly (e.g. for a dummy variable the mean was not provided.) The mean of use of technological

processes is relatively low with mean = 2.70. For this variable the number of times firms replied with ‘yes’ was summed and this created a ranking. Firms could rank between 0 and 8 and therefore this mean could be considered as relatively low. The mean of ‘regulatory environment’ is also interesting as it is also a summed variable, which created a ranking. Firms could rank between 0 and 5, with 5 indicating that firms have encountered many legal problems or complications. As a result a mean of 4.6 is very high.

Looking at ‘perceived use of E-business’, ‘firm size’ and ‘geographical scope’, these variables each have subgroups that have been ranked. Therefore it is interesting to see the distribution of cases within each group. This is represented in the last column ‘% of total N’, which shows what percentage the subgroup is, of the total cases included for this variable. These percentages do not add up to 100% exactly, this missing percentage indicates the missing variables, and is never higher than 1.4%. For the dependent variable we see that only 16% of the firms have indicated E-business as a significant part of how their business operates today. Another notable percentage is that for ‘IT expertise’, 85.7% of the firms indicated that they had not recruited or had not tried to recruit staff with ICT or E-business skills. Thus, the number of firms that recruited IT expertise is relatively low.

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which only includes 163 cases. The variable’ GDP per capita’ represents the GDP of each country included in the research and the variable ‘ease of doing business’ represents a ranking from 1 to 7, ranking the countries included in this research.

Table 3 shows all correlations between the dependent variable and the independent variables. We see that all independent variables provide a significant positive correlation with the dependent variable at α = 0.01 level, with the exception of the regulatory environment which shows a negative significant correlation. For firm size and the regulatory environment this correlation is opposite to the hypothesized correlation.

Table 4 provides an overview of the correlations between the dependent variable and the control variables. For firm sectors we see that some are significantly correlated to the dependent variable, either positively or negatively. This can be explained by the use of dummy variables for the sectors. Each correlation indicates the specific industry (=1) or one of the other 9 industries (=0). Looking at the food & beverages sector the correlation is

significant with -.094. This means that compared to the other industries the Food & Beverages industries use significantly less E-business. This is also seen for the textile, construction and automotive industries. The tourism sector and the IT services sector are both significantly, positively related to perceived use of E-business. Looking at ‘GDP per capita’ and ‘ease of doing business’ we see a small positive correlation with the dependent variable at α = 0.05. Considering the significant correlations of the control variables it is important to include them as control variables in the regressions.

Last, in Appendix C we show the correlations for each TOE-element with the dependent variable and the control variables. In general the correlations for the sectors are relatively similar to those presented in table 5. Appendix C.1 shows the correlation matrix for the technological elements. The control variable ‘GDP per capita’ is only significantly

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geographical firm scope. Appendix C.3shows that both ‘GDP per capita’ and ‘ease of doing business’ should be considered as control variables for the environmental elements.

Table 1:

Descriptive statistics

Dependent and Independent variables

Variable Variable

type

Min Max Mean SD N % of total N 1 Perceived E-business use Scale 1 3 1.76 .746 5146

a None at all - - - 2103 40.3%

b Some part - - - 2171 41.6%

c Significant part - - - 872 16.7%

2 Tech_processes Scale 0 8 2.70 1.642 3742 -

3 Tech_innovation Scale 0 2 1.18 .804 1582 -

4. Firm size Scale 1 4 2.15 1.010 5218 -

a Micro firm - - - 1796 34.4%

b Small firm - - - 1373 26.3%

c Medium firm - - - 1516 29.1%

d Large firm - - - 533 10.2%

5 Firm scope Scale 1 3 1.86 .744 5150 -

a Regional market - - - 1752 33.6% b National market - - - 2388 45.8% c International market - - - 1010 19.4% 6 IT expertise Dummy 0 1 - - 5148 - 0 = no - - - 4473 85.7% 1 = yes - - - 675 12.9%

7 Supplier pressure Scale 1 4 2.32 1.088 3022 -

8 Customer pressure Scale 1 4 2.88 1.107 3029 -

9 Competitive pressure Scale 1 4 2.51 1.094 3023 -

10 Regulatory environment Scale 0 5 4.60 1.079 3115 -

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27 Table 2:

Descriptive statistics

Dependent and Control variables

Variable Variable

type

Min Max Mean SD N % of

total N Perceived E-business use Scale 1 3 1.76 .746 5146

Food & Beverages Dummy 0 1 - - 571 10,9%

Textile industries Dummy 0 1 - - 561 10,8%

Publishing & Print Dummy 0 1 - - 563 10,8%

Pharmaceuticals Dummy 0 1 - - 532 10,2%

Machinery & Equip Dummy 0 1 - - 565 10,8%

Automotive Dummy 0 1 - - 565 10,8%

Aerospace Dummy 0 1 - - 163 3,1%

Construction Dummy 0 1 - - 566 10,8%

Tourism Dummy 0 1 - - 567 10,9%

IT services Dummy 0 1 - - 565 10,8%

GDP per Capita (US$) Scale 7.976,10 39.934,90 26.933,68 31.973,10 5218 -

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28 Table 3:

Correlation matrix

Dependent and Independent variables

Variable 1 2 3 4 5 6 7 8 9 10

1 Perceived E-business use -

2 Tech_processes .237** - 3 Tech_innovation .223** .224** - 4 Firm size .133** .388** .017 - 5 Firm scope .166** .200** .006 .292** - 6 IT expertise .192** .309** .223** .200** .092** - 7 Supplier pressure .173** .090** .073* .048** .054** .055** - 8 Customer pressure .236** .147** .096** .098** .123** .099** .417** - 9 Competitive pressure .178** .054** .092** .047* .092** .030 .341** .434** - 10 Regulatory environment -.085** -.163** -.067* -.122** -.094** -.119** -.081** -.075** -.071** -

** Correlation is significant at the 0.01 level (2-tailed).

* Correlation is significant at the 0.05 level (2-tailed).

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29 Table 4:

Correlation matrix

Dependent and Control variables

Variable 1 2 3 4 5 6 7 8 9 10 11 12 13

1 Perceived E-business use -

2 Food & Beverages -.094** -

3 Textile industries -.061** -.122** -

4 Publishing & Print .027 -.122** -.121** -

5 Pharmaceuticals .004 -.118** -.117** -.117** -

6 Machinery & Equip -.027 -.122** -.121** -.121** -.117** -

7 Automotive -.043** -.122** -.121** -.121** -.117** -.121** - 8 Aerospace .013 -.063** -.062** -.062** -.061** -.063** -.063** - 9 Construction -.093** -.122** -.121** -.121** -.118** -.122** -.122** -.063** - 10 Tourism .081** -.122** -.121** -.121** -.118** -.122** -.122** -.063** -.122** - 11 IT services .198** -.122** -.121** -.121** -.117** -.121** -.121** -.063** -.122** -.122** - 12 GDP per capita .034* -.006 -.009 -.007 .017 -.008 -.007 .071** -.006 -.005 -.008 - 13 Ease of doing business .031* -.006 -.004 .000 .002 -.004 -.002 .032* .000 -.001 -.002 .433** -

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To test the hypotheses an ordinary least squares (OLS) multiple regression was run for each of the TOE-framework elements (Cooper & Schindler, 2008). The ordinary least squares (OLS) multiple regression allows us to learn more about the relationship between perceived use of E-business and the independent variables (Keller, 2008). To see how all TOE-elements together relate to perceived use of E-business, this regression was run again including all

TOE-elements. In each regression the first model runs the control variables with ‘perceived use of E-business’ and the second model runs the TOE-elements. From the sector control variables the pharmaceuticals sector is excluded to prevent the dummy trap (Garavaglia & Sharma, 1998). The pharmaceutical sector is used as the basic category as it does not have significant positive or negative correlation with the dependent variable. The dummy trap is caused when the value of one variable can be predicted from other variables. If all the sector variables were included, their sum would be equal to 1, which would result in perfect multicollinearity. Furthermore, we would not be able to interpret the results, as the coefficient of each sector variable represents the increase or decrease in the intercept relative to that for the basic category. Without excluding a sector there will not be a basic category for such comparison (Dougherty, 2012). We should be careful with the interpretation of these results, as the results will indicate whether sector differences are present, but do not allow us to make any specific conclusions about these differences. To check for any issues regarding multicollinearity, we account for the variance inflation factors (VIF) in each regression. When VIF<10 there is no problematic influence of collinearity found (Meyers, 1990). For all variables, in all models, VIF is smaller than 10 and no problematic influence of collinearity is found.

To measure the relationship of the control variables on the perceived use of E-business, a separate regression was run, which is presented in appendix D.1. From this regression we see that there are significant sector differences. Furthermore, the results show GDP per capita to be significant at α = 0.01 level . The ease of doing business does not show a significant relationship. To examine the effect of excluding the sector variables as control variables from the regression, we ran a regression without the sector variables and including GDP per capita, ease of doing business and all TOE-elements. The results of this regression are presented in Appendix D.2. We see R² = .005 when excluding the sector variables and R²= .075 (table 8) when including the sector variables. Therefore, the sector variables were

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Table 5:

Regression results

Control variables & Technological elements

Model 1 P = .000 2 P = .000

Β(SE) VIF Β(SE) VIF

(Constant) 1.780***(.07 7) 1.517***(.081 ) Food_Beverages -.172* (.083) 1.555 -.180* (.081) 1.562 Textile_industries -.073 (.084) 1.533 -.070* (.083) 1.543 Publishing_Print .060 (.079) 1.641 .003 (.079) 1.732 Mach_Equip_man -.049 (.080) 1.618 -.081 (.078) 1.626 Automotive -.052 (.079) 1.641 -.071* (.077) 1.651 Aerospace -.042 (.134) 1.163 -4.400 (.132) 1.176 Construction -.023 (.102) 1.308 -.060 (.100) 1.319 Tourism .239** (.080) 1.619 .220** (.079) 1.668 IT_services .397*** (.069) 2.001 .228** (.073) 2.344 GDP per Capita (US$) 7.228 (.000) 1.158 -7.957 (.000) 1.169 Ease of doing business .029* (.011) 1.146 .025* (.011) 1.149 Tech_processes .077***(.011) 1.149 Tech_innovation .089** (.027) 1.274 R² .073 .121

***. Significant at the 0.001 level **. Significant at the 0.01 level *. Significant at the 0.05 level.

In the regression shown in table 5, the control variables and the technological elements were entered into the regression. When looking at model 2 which includes the control variables and the technological elements, we see that the model is significant at the p = .000 and R² = .121. We see an increase in the variability of the dependent variable explained when adding the technological elements into the regression. This means that the technological elements make a contribution in explaining perceived use of E-business. Looking at the control variables, firm sector should be interpreted with care as the results are compared to the basic sector, the pharmaceuticals sector. However, from the results we can conclude that there are significant firm sector differences in relation to perceived use of E-business. The food and beverages sector has a significant negative relationship with perceived use of E-business, whereas the tourism and IT services sectors show a significant positive relationship. Furthermore, the ease of doing business is significantly, positively related to perceived use of E-business. The hypotheses are considered below.

Hypothesis 1a predicts that a higher level of use of technological processes is

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show a positive relationship between the level of use of technological processes and perceived use of E-business ( β = .077, p = .000).

Hypothesis 1b suggests a positive relationship between higher adoption of

technological innovations and perceived use of E-business. This is supported by the results at α = 0.01 level (β = .089, p = .001).

In the second regression the control variables and the organizational elements are entered into the regression analysis as presented in table 6. The model is found to be significant at the p = .000 and R² = .111. This indicates that the organizational elements make a contribution to explaining the perceived use of E-business. The firm sectors show significant differences in their relationship with perceived use of E-business, indicating that there are differences between sectors. GDP per capita and ease of doing business both show a significant positive relationship with perceived use of E-business. The organizational elements all show a significant positive relationship with perceived use of E-business. This will be discussed briefly below.

Table 6:

Control variables & Organizational elements

Model 1 P = .000 2 P = .000

(Constant) 1.690***(.041) 1.262***(.051) Food_Beverages -.197***(.043) 1.846 -.146** (.042) 1.897 Textile_industries -.133** (.043) 1.842 -.134** (.042) 1.842 Publishing_Print .047 (.043) 1.843 .074 (.042) 1.893 Mach_Equip_man -.063 (.043) 1.854 -.068 (.042) 1.854 Automotive -.099* (.043) 1.835 -.093* (.042) 1.840 Aerospace .027 (.064) 1.270 .011 (.062) 1.284 Construction -.197*** (.043) 1.845 -.101* (.043) 1.967 Tourism .160*** (.043) 1.832 .191*** (.042) 1.857 IT_services .397*** (.043) 1.855 .358*** (.043) 1.955 GDP per Capita (US$) 1.835 (.000) 1.236 2.372* (.000) 1.237 Ease of doing business .007 (.005) 1.230 .006* (.005) 1.233 Firm size .055***(.010) 1.161 Firm scope .135***(.015) 1.263 IT expertise .254***(.030) 1.128

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Hypothesis 2a poses that firm size is negatively related to perceived use of E-business.

The results show that there is a significant positive relationship between firm size and perceived use of E-business (β = .055, p = .000). The results do not support hypothesis 2a.

Hypothesis 2b indicates that a geographically broader firm scope is expected to

positively relate to perceived use of E-business. This hypothesis is supported by the data as the results show (β = .135 p = .000).

Hypothesis 2c proposes that firms that have recruited or have tried to recruit IT

professionals in the last 12 months have a higher perceived use of E-business. The results indicate a positive relationship between the recruitment of IT expertise and the perceived use of E-business (β = .254, p = .000).

Table 7:

Control variables & Environmental elements

Model 1 P = .000 2 P = .000

(Constant) 2.230***(.03 3) 1.948***(.057 ) Food_Beverages -.123** (.037) 1.719 -.133***(.036) 1.721 Textile_industries -.049 (.037) 1.741 -.053 (.035) 1.743 Publishing_Print .010 (.035) 1.893 -.011 (.033) 1.898 Mach_Equip_man -.067 (.035) 1.831 -.085* (.034) 1.837 Automotive .020 (.037) 1.732 -.012 (.036) 1.738 Aerospace -.002 (.052) 1.284 .002 (.050) 1.285 Construction -.079* (.038) 1.682 -.067 (.036) 1.686 Tourism .045 (.034) 1.965 .020 (.033) 1.975 IT_services .203***(.033) 2.061 .169***(.032) 2.073 GDP per Capita (US$) 4.230***(.00

0) 1.314 5.670***(.000 ) 1.345 Ease of doing business -.016***(.005) 1.303 -.021***(.004) 1.321 Supplier pressure .039***(.009) 1.281 Customer pressure .069***(.009) 1.389 Competitive pressure .039***(.009) 1.290 Regulatory environment -.024** (.008) 1.025 R² .051 .126

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of E-business. Significant firm sector differences are found in the results. Furthermore, GDP per capita is significantly positive related to perceived use of E-business. The ease of doing business is significantly negative related to the dependent variable. The environmental elements are discussed below.

Hypothesis 3a predicts perceived supplier pressure is positively related to higher

perceived use of E-business. A significant relationship is found between supplier pressure and perceived use of E-business (β = .039, p = .000).

Hypothesis 3b suggests that higher customer pressure leads to higher perceived use of

E-business. A positive relationship is found between customer pressure and perceived use of E-business at (β = .069, p = .000).

Hypothesis 3c proposes perceived use of E-business to be positively influenced by

higher perceived competitive pressure. A positive relationship is found between competitive pressure and perceived use of E-business (β = .039, p = .000).

Hypothesis 3d indicates that the regulatory environment is negatively related to

perceived use of E-business. The results show a negative relationship between a more

supportive regulatory environment and perceived use of E-business α = 0.01 level (β = -.024, p = .004) and do not support hypothesis 3d.

To summarize, when looking at the results of table 5, 6 and78 we see that hypotheses 1a, 1b, 2b, 2c, 3a, 3b and 3c are supported by the results. Hypotheses 2a and 3d are not supported by the results. This means that the hypotheses about firm size and the regulatory environment are not supported by the results. When looking at the control variables there are some interesting results. The control variable firm sector shows differences between the regression results of the different TOE-elements. The machinery & equipment manufacturing sector is only significantly negatively related to the dependent variable when the environmental elements are entered into the regression. The same is found for the construction sector when the

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be significantly related to the perceived use of E-business, although for firm size and the regulatory environment the relationship is opposite of what was expected.

Table 8:

Control variables & TOE-elements

Model 1 P = .000 2 P = .000

(Constant) 2.243***(.06 0) 1.789***(.113 ) Food_Beverages -.125 (.069) 1.508 -.094 (.066) 1.581 Textile_industries -.039 (.069) 1.517 -.025 (.065) 1.532 Publishing_Print .089 (.063) 1.657 .090 (.062) 1.787 Mach_Equip_man -.100 (.064) 1.627 -.113 (.061) 1.646 Automotive -.016 (.065) 1.600 -.035 (.062) 1.642 Aerospace .054 (.114) 1.144 .097 (.108) 1.156 Construction -.084 (.080) 1.327 -.043 (.077) 1.385 Tourism .083 (.062) 1.709 .101 (.059) 1.775 IT_services .247***(.054) 2.160 .160** (.056) 2.681 GDP per Capita (US$) 2.941 (.000) 1.192 4.502**(.000) 1.250 Ease of doing business -.005 (.009) 1.191 -.008 (.009) 1.216 Tech_processes .023* (.009) 1.413 Tech_innovation .020 (.021) 1.325 Firm size -.054** (.016) 1.324 Firm scope .064** (.023) 1.181 IT expertise .072* (.035) 1.206 Supplier pressure .052** (.015) 1.230 Customer pressure .057** (.017) 1.308 Competitive pressure .056***(.015) 1.260 Regulatory environment -.028* (.012) 1.058 R² .075 .186

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Looking at the TOE-framework elements, we see that all TOE-elements are

significantly related to the ‘perceived use of E-business’ when they are entered in a separate regression. The organizational and environmental elements remain significantly related to the dependent variable after all elements are entered into the regression. However, for the

technological elements we see that technological innovation is now found not to be

significantly related to perceived use of E-business (β = .020, p = .352). Interestingly we see that firm size is now significantly negatively related to the use of E-business (β = -.054, p = .001)., where it is significantly positively related when we run a regression for the

organizational elements (β = .055, p = .000).

Finally, this research has also examined country differences. First, a one way ANOVA (F-test) is conducted to research whether statistically significant differences exist among the perceived use of E-business in different countries. The descriptive statistics are provided in appendix D.3. The results show statistically significant differences among the seven countries, F ( 6, 5139 ) = 56.139, p = .000. Levene’s test for homogeneity of variance indicates that the assumption for equal variances is not met ( p = .000) (Pallant, 2010). Therefore the post-hoc test of Games-Howell is conducted which reveals statistically significant differences between some countries (appendix D.4). Germany shows significant differences with all other

countries ( M = 2.11, SD = .70 ), and France ( mean = 1.53, SD = .64) and Italy (M = 1.87, SD = .79 ) with five other countries.

As mentioned earlier, a regression was also run without the sector variables as control variables, of which the results are shown in Appendix D.2 The results are very similar, with the exception of technological innovation which is not significant when the sector variables are included (table 8) but is found to be significant when the sector variables are excluded α = 0.01 level (β = .056, p = .005). The full model including the sector variables as control

variables has a higher R² than the model excluding the sector variables as control variables ( R² =.186 and R² =.154 respectively).

Determining the use of E-business in European firms in light of the TOE-framework