Adoption of Internet Technologies in Small Business - CiteSeerX

Adoption of Internet Technologies in Small Business Jungwoo Lee Yonsei University [email protected] Seung Ik Baek Hanyang University [email protected] Abstract The adoption of information technology represents a problem of magnitude to small business entrepreneurs. Situation they are facing is different from larger corporations, making technology adoption behavior different from them. This study reports on antecedent drivers of small business adoption of Internet technologies. A behavioral model of innovation acceptance was developed with its foundation on previous literature on technology acceptance and innovation adoption research focused on small businesses. The model posits relationships of relative advantage of using IT, compatibility, ease of use, computer selfefficacy, financial slack of the firm, innovativeness of the firm, image of IT, and competitive pressure against the adoption behavior of four different Internet technologies -- email, business homepage, e-sales and e-procurement. The results confirm the strong association of these antecedents with the adoption behavior and reveal different patterns of adoption behavior across different technologies. Different compositions of these antecedents provide insights on theoretical development of Internet technology acceptance model. Theoretical and practical implications are discussed. Keywords: small business, information technology adoption, innovation adoption, Internet technology, Internet adoption, technology acceptance, technology adoption 1.

Introduction

Small businesses contribute more and more to the national and international economies throughout the world. In US, small businesses employ 53 percent of the private work force, generate 47 percent of all sales, are responsible for 50 percent of the private sector gross domestic product, and produced an estimated 75 percent of the 2.5 million new jobs created during 1995 (Small Business Administration, 1996). Although there is no reason to believe that information technology (IT) is any more critical to large corporations than to small businesses, the challenges faced by small businesses are different from those of large corporations. In this regard, through a literature review, this research selected most well received antecedents of adoption (perceived relative advantage of IT, compatibility, ease of use of IT, computer self-efficacy of the owner, financial slack of the firm, original innovative orientation of the firm, image about IT and competitive pressure) and empirically tests the impact of these antecedents on the adoption of four different Internet technologies (email, business homepage, e-sales, and e-procurement) in small business environment. Seventy-one small independent retailers across four different industries -- appliance, electronics, furniture, and hobby -- have participated in this study.

The paper is organized into three sections. First, research questions and theoretical backgrounds are presented. The next section describes the study design and data analysis, and presents results. Finally, implications of the findings and conclusions are offered. 2.

Research Questions and theoretical background

This research studies the adoption of new Internet technologies in small businesses. Focus of the study is on identifying individual and organizational antecedents of technology adoption behavior through extant literature review, and empirically testing the theoretical relationship of these antecedents against the adoption behavior in the context of small businesses. Prior research has developed a long list of drivers for small business IT adoption, but close inspection of previous literature in the area of small business technology adoption revealed that only a handful of factors were found to actually influence the adoption behaviors: innovativeness and IS knowledge (Thong, 1999), relative advantage (Cragg and King, 1993; Premkumar and Roberts, 1999; Thong, 1999), firm size (Bridge and Peel, 1999; Premkumar and Roberts, 1999), ease of use and perceived usefulness (Iacovou, et al., 1995; Igbaria, et al., 1998; Igbaria, et al., 1997), top management support (Cragg and King, 1993; Foong, 1999; Premkumar and Roberts, 1999), external pressure (Iacovou, et al., 1995; Premkumar and Roberts, 1999), and intensity of strategic planning (Bridge and Peel, 1999). In relation to the outcome measure of adoption behavior, different measures have been developed in the small business literature: adoption decisions (Thong, 1999), degree of adoption (Cragg and King, 1993; Iacovou, et al., 1995; Julien and Raymond, 1994; Lefebvre, et al., 1995; Premkumar and Roberts, 1999; Thong, 1999), system use (Bridge and Peel, 1999; Foong, 1999; Igbaria, et al., 1998), satisfaction (Foong, 1999; Palvia and Palvia, 1999) and adoption intention (Harrison, et al., 1997). However, none of these differentiates different types of technologies except in (Premkumar and Roberts, 1999). Each of these adoption measures was focused on specific technologies, such as EDI (Iacovou, et al., 1995) or in general information technology. The research model utilized to examine the research questions is presented in Figure 1. Each of the major components and linkages in this research model is discussed below. Figure 1. Research model Relative Advantage Compatibility Ease of Use Computer Self-Efficacy Financial Slack Innovativeness Image Competitive Pressure

Internet Adoption

Relative advantage "… refers to the degree to which an innovation is perceived as being better than the idea it supersedes (Rogers, 1983, p. 53)." Studies show that organizations are more likely to adopt innovations when there are experts present in the firm that identify an innovation as desirable and support its implementation. Further, it has been found that those who allocate organizational resources influence innovation adoption (Baldridge and Burnham, 1975; Hage and Dewar, 1973; Kimberly and Evanisko, 1981). In entrepreneurial ventures and small firms, these two responsibilities reside with the owner-manager (Bigoness and Perreault, 1981; Fennell, 1984; Moch and Morse, 1977). To the degree that the owner perceives an innovation as offering a relative advantage over the firm’s current state, it is more likely to be adopted and implemented. This view has received empirical support in small business research (Cragg and King, 1993; Thong, 1999) as well as in the innovation diffusion literature (Prescott and Conger, 1995; Tornatzky and Fleischer, 1990). If the small firm owner-manager believes that IT innovation will enhance the efficiency and effectiveness of his/her business or afford him/her more control over the business, he/she will be more likely to adopt the innovation. H 1.

The greater the perceived relative advantage of using information technologies, the more likely they will adopt Internet technologies.

Compatibility. The innovation’s compatibility with the business is defined as the degree to which it is perceived as being consistent with the existing values, past experiences and needs of the potential adopter (Tornatzky and Fleischer, 1990). In this regard, it is the ownermanager’s perception about whether or not the changes incurred by technology adoption are compatible with its current values and systems that determines the firms adoption behavior. H 2.

The more the owner feels that IT is compatible with their business, the more likely the firm will adopt the Internet technologies.

Ease of use. Perceived ease of use is defined as “the degree to which a person believes that using a particular system would be free of effort” (Davis, 1989, p.320). When all other conditions are equal, it is expected that any technology perceived to be easier to use than another is more likely to be adopted (Moore and Benbasat, 1991). H 3.

The more the owner feels that IT is easy to use, the more likely the firm will adopt the Internet technologies.

Computer Self-Efficacy. Bandura defined self-efficacy as “judgments of how well one can execute courses of action required to deal with prospective situations” (Bandura, 1982, p.122). Extending this concept of self-efficacy, Compeau defined computer self-efficacy as “judgment of one’s capability to use a computer” (Compeau and Higgins, 1995, p. 192). In the small business context, when making decision to adopt IT, the owner-manger may hold differing beliefs about their capabilities to perform tasks using technology they are adopting. Depending upon how strong their computer self-efficacy is, they will feel more confident in adopting new technologies. In this regard, the computer self-efficacy the owner maintains will influence the adoption behavior itself. H 4.

The higher the computer self-efficacy of the owner, the more likely the firm will adopt the Internet technologies.

Financial Slack. It is posited here that the adoption phenomenon in small business can also be explained by the existence of a slack in the firm’s financial situation. Financial slack refers to financial resources in excess of what is required to maintain the organization (Ang and Straub, 1998). Bourgeois defined it as a cushion of excess resources available in an

organization that will either solve many organization problems or facilitate the pursuit of goals outside the realm of those dictated by optimization principles (Bourgeois, 1981). The reasoning is that when a firm has slack resources, firms may enlarge the scale and scope of their operations by deploying slack resources toward building up technology resources. In other words, it is easier for them to deploy technologies when the firm maintains slacks in resources. Conversely, when slack resources are low, firms are likely to resist investing in IT. H 5.

The greater the firm’s financial slack, the more likely the firm will adopt the Internet technologies.

Innovativeness. IT is not the first technological innovation experienced by business. Historically, modernization and industrialization in the last century have firms involved in the adoption of technological innovations in production and administration. In the innovation literature, innovation is often classified into two categories: administrative innovation and product innovation. Product innovation in manufacturing firms includes those resources associated with a firm's research and development efforts, such as research facilities and the technically skilled individuals employed within them. In a retail service setting, this product innovation takes the form of new product offerings and the development of new markets products (Cooper and Schendel, 1976; Meyer and Goes, 1988). In contrast, administrative innovation involves changes in structure and managerial processes. A firm's ability to devise new organizational forms and processes enhances its ability to exploit new opportunities internally, such as technological advancement, and externally, such as new or expanding markets (Damanpour, 1988; Ibarra, 1993; Kimberly and Evanisko, 1981; Sanchez, 1995). In the context of small firms, it seems that opportunities for administrative innovation may be limited. These ventures are operating with few employees, often directly supervised by the owner-manager. Organizational structure is very flat, decision-making is completely centralized, and the owner-manager leads the product innovation and market expansion activities, while focusing less on administrative innovation. Here, the firm's innovativeness is examined through investigation of small firm product innovation. In this regard, it is posited here that the firm's existing proclivity toward (product) innovativeness may influence further innovation adoption behavior in relation to the Internet technologies. H 6.

The greater the firm’s innovativeness is, the more likely the firm will adopt the Internet technologies.

Image. Moore and Benbasat (1991) suggest that 'image' associated with users of IT and IT itself is an important determinant of the adoption decision. Rogers (1983) also suggests 'observability' as a general attribute of innovation that influences adoption decisions. The more visible the outcome of the innovation is, the more likely it is that people will adopt it. Harrison and Mykytyn (1997) found that the subjective norms, maintained by peers and society, strongly influence the intention to adopt IT in small businesses. This suggests that the IT adoption decision in a small business context is not strictly based on cost-benefit analysis, but it may also be based on perceived impressions that a firm projects towards its internal and external environment by having IT resources. The owner-managers may receive some pressure to adopt Internet technologies in order to make the firm seem more prestigious, and as the owner-manager is the most critical strategic decision maker in the small business, the internal and external pressure they feel may be an important determinant of IT adoption. H 7.

The more novel image IT projects, the more likely the firm will adopt the Internet technologies.

Competitive Pressure. Aside from internal pressures identified above, there are cases where technology helps firms to obtain advantageous competitive position. Competition in the

adopter’s industry is generally perceived to positively influence the adoption of innovation. This would be even more evident if the innovation directly affects the competition position. H 8. 3.

The greater the competitive pressure, the more likely the Internet technologies will be adopted.

Methodology

3.1 Measures Details of measures are summarized in Table 1. All of these measures were adopted from existing scales whose validity and reliability have been previously demonstrated, as discussed in the previous section. The firm’s adoption level was measured as binary variables, asking whether or not they have adopted each Internet technology – email, homepage, e-sales, and eprocurement. Table 1. Item description Construct (reliability α)

Item Description

Relative advantage (0.95)

IT enhances the effectiveness of my business. IT enhances the efficiency of my business. IT gives the business owner greater control.

Compatibility

IT is compatible with all aspects of my business.

Ease of Use

IT is easy to use.

Computer Self-Efficacy

Personally I can complete a job using the software package even if there is no one around to help me out

Financial Slack

I have established lines of credit for my business I can easily get outside funding if I need it.

Innovativeness (0.73)

My company (never, seldom, occasionally, frequently, very often) offers new product lines or services My company (never, seldom, occasionally, frequently, very often) targets new markets or segments My company (never, seldom, occasionally, frequently, very often) create products/services for the market before other competitors do so

Image (0.81)

People in my organization who use computers have more prestige than those who do not. People in my organization who use computers have a high profile. Having a computer is a status symbol in my organization

Competitive Pressure

My company is using information technology due to the competitive pressure

3.2 Data Collection and Sample The data for this study was collected from seminar participants at two national meetings held in a large southwestern city in the U.S. Those attending the seminars were owner-managers of small independent retail stores representing the appliance, furniture, electronics, and hobby industries. The individuals responding were the top decision-makers in their firms. One hundred and twenty-five (125) owners attended the first seminar for retailers in the appliance, furniture, and electronics industries and sixty-three (63) in the second seminar for retailers in

the hobby industry: one hundred and eighty-eight (188) participants overall. Thirty-six completed surveys were returned in the first seminar and thirty-five from the latter. A total of seventy-one surveys were returned for a response rate of 37.8% (28.9 and 55.5%, respectively). 4

Results

4.1 Data Analysis 4.1.1 Sample characteristics The characteristics of the sample are shown below in Table 2. Largely three industry sectors were included in the sample: hobby, appliance and furniture. Hobby industry was the largest category in the sample. Firm size varies from less than five employees to more than 30 employees, however more than 80% of the sample employs less than 15 people. As part of the demographic survey, a question was asked whether or not it is a family business, about 97% of the sample classify themselves as family business. Table 2. Sample characteristics Number of firms Company size Less than or equal to 5 6 to 10 11 to 15 16 to 20 21 to 25 26 t o30 More than 30 Industry Appliance Furniture Electronics Hobby Annual sales revenue Less than 250K 250K to 500K 500K to 1 M 1M to 2.5M 2.5M to 5M More than 5M Years in business 1 – 10 years 11 – 20 years 21 – 30 years 31 – 40 years 41 – 50 years More than 50 years

4.1.2

31 18 9 1 4 3 5 21 12 6 36 6 20 17 15 13 1 20 10 11 6 9 14

Assessment of validity and reliability

Content validity was established through the extensive process of item selection and refinement. The items used for measuring the constructs were derived from operationalization used in prior empirical studies and were adapted to suit this research context. A team of researchers involved in the larger project scrutinized the items and ensured that it measures the appropriate domain of the construct and covered the complete domain of the construct.

Convergent and discriminant validities were evaluated using principal component factor analysis. Principal component analysis of multi-item indicators was used to evaluate if the theorized items for a construct converge together (convergent validity) and at the same time if the items are not loaded onto dimensions other than intended (discriminant validity). The results of factor analysis are shown in Table 3. Table 3. Validity and reliability properties Mean S.D. Relative advantage 5.371 1.294 Compatibility 4.521 1.520 Ease of use 4.200 1.575 Computer self-efficacy 4.114 1.838 Financial slack 3.917 1.875 Innovativeness 3.460 .803 Image 3.219 1.416 Competitive Pressure 3.729 1.777

Skewness -.714 -.242 .049 -.043 .533 -.173 .267 .042

S.E. .287 .285 .287 .287 .283 .285 .287 .287

Kurtosis .682 -.168 -.694 -.913 -1.050 -.753 -.480 -1.004

S.E. .566 .563 .566 .566 .559 .563 .566 .566

Items 3 1 1 1 2 3 3 1

α 0.95

0.73 0.81

Table 4. Factor analysis

Relative advantage 2 Relative advantage 3 Relative advantage 1 Innovativeness 2 Innovativeness 3 Innovativeness 1 Image 3 Image 2 Image 1

4.1.3

Components 1 2 .961 .936 .921 .817 .799 .794

3

.878 .853 .752

Status of Internet technologies used by small businesses

One of the broader objectives of this study was to determine the level of Internet technology usage. Figure 2 illustrates the use of Internet technologies in small businesses. Email is the most prevalent Internet technology used by over 73% of the participants. This is followed by the business homepages used by 69% of the firm. 53.5% of the sample reported sales through the Internet, and the electronic procurement is the least adopted Internet technology among the four. Only 33.8% of the firms adopted procurement transactions on the Internet.

Figure 2. Internet technologies in use by subjects 100.0% 73.2%

69.0% 53.5%

50.0%

33.8% 12.7%

rn et er In te th

O

ro cu re m en t

e

cp

cs al El

ec tro ni

ec tro ni

El

H

Em ai

l

om ep ag e

0.0%

Table 5. Discriminant Analysis – Electronic Mail Variable Relative Advantage Compatibility Ease of Use Self Efficacy Financial slack Innovativeness Image Competitive Pressure

Wilk’s Lambda 0.907 0.881 0.956 0.888 0.863 0.922 0.942 1.000

Discrim Coef 0.174 0.108 0.145 0.256 0.738 0.516 0.397 -0.153

Discrim Loading 0.422 0.482 0.283 0.466 0.523 0.381 0.326 -0.016

Non adopters Means SD 5.599 1.221 4.837 1.344 4.388 1.441 4.490 1.816 4.367 1.965 3.605 0.735 3.340 1.474 3.714 1.848

Adopters Means SD 4.686 1.377 3.647 1.693 3.647 1.766 3.059 1.713 2.765 1.091 3.098 0.888 2.569 1.040 3.765 1.751

Sig. 0.013 0.005 0.090 0.006 0.002 0.023 0.051 0.922

Classification Accuracy Original Count % Overall Accuracy Chance Accuracy Sig. Validation Accuracy

Email Non adopters Adopters Non adopters Adopters

Prediction Count Non adopters Adopters 13 4 9 40 76.5 23.5 18.4 81.6 86.4% 61.7% 0.001 Press’s Q 80.3%

Total 17 49 100.0 100.0 24.242

Model Statistics Wilk’s Lambda 0.633 χ2 27.440 d.f. 8 Sig. 0.001

Table 6. Discriminant Analysis – Business Homepage Variable Relative Advantage Compatibility Ease of Use Self Efficacy Financial slack Innovativeness Image Competitive Pressure

Wilk's Lambda 0.831 0.884 0.971 0.926 0.969 0.960 0.931 0.955

Discrim Coef 0.645 -0.053 -0.032 0.098 0.425 0.431 0.318 0.276

Discrim Loading 0.746 0.600 0.285 0.467 0.295 0.338 0.451 0.359



Sig. 0.001 0.005 0.172 0.027 0.158 0.107 0.033 0.088


Home pages Non adopters Adopters Non adopters Adopters

Prediction Count Non adopters Adopters 13 7 11 35 65.0 35.0 23.9 76.1 84.8% 57.8% 0.001 Press’s Q

Total 20 46 100.0 100.0 13.63 6


81.8%

4.2 Results To test the research hypotheses, the discriminant analysis technique was used. This technique provides a statistical procedure to identify each predictor’s contribution to a linear function that best discriminate between two or multiple groups. Discriminant analysis involves deriving a linear combination of independent variables that best discriminate between the pre-defined groups. This is the appropriate technique when the dependent variable is categorical such as adopters and non-adopters. The objective is to maximize between-group variances compared to within-group variances based on a series of discriminant scores generated by a linear combination of independent variables, so that the discriminant function separates the groups well. Separate discriminant models were generated for the four Internet technologies. The value of Wilkes Lambda, value and the level of significance are shown in Tables 5 through 8. Three models (e-mail, business homepage and e-sales) were significant at the critical level of 0.05 and the e-procurement model was significant at 0.1 level.

Table 7. Discriminant Analysis – Electronic Sales Variable Relative Advantage Compatibility Ease of Use Self Efficacy Financial slack Innovativeness Image Competitive Pressure

Wilk’s Lambda 0.874 0.904 0.952 0.907 0.921 0.976 0.939 1.000

Discrim Coef 0.534 -0.096 0.092 0.222 0.617 0.287 0.470 -0.257

Discrim Loading 0.622 0.534 0.367 0.523 0.481 0.256 0.417 -0.013



Sig. 0.003 0.011 0.077 0.013 0.022 0.215 0.045 0.951

Classification Accuracy Original Count %

Non adopters Adopters Non adopters Adopters

Overall Accuracy Chance Accuracy Sig. Validation Accuracy


Total 31 35 100.0 100.0 17.515


Table 8. Discriminant analysis – Electronic Procurement Variable Relative Advantage Compatibility Ease of Use Self Efficacy Financial slack Innovativeness Image Competitive Pressure

Wilk’s Lambda .964 .942 .906 .901 .935 .997 .952 .999

Discrim Coef -0.154 0.132 0.451 0.417 0.650 -0.155 0.288 -0.073

Discrim Loading 0.364 0.470 0.608 0.628 0.498 -0.111 0.426 0.068



Sig. 0.129 0.051 0.012 0.010 0.039 0.642 0.076 0.776


Non adopters Adopters Non adopters Adopters


Total 44 22 100.0 100.0 19.636


The standardized discriminant coefficients and discriminant loadings for the variables are given in the tables. Univariate statistics in terms of group wise means and F-value significance on equality of means are also provided for comparative analysis. Discriminant loadings measure the simple linear correlation between each predictor and the derived discriminant function. While discriminant loading evaluates the significance of the variables, classification test determines the ability of the model to classify accurately. The classification test compares the classificatory accuracy of the discriminant model to the chance model. Change accuracy is determined by the formula p2 - (1-p)2 where p is the

proportion of the sample in the first group. The chance accuracy was calculated and compared with the value for the discriminant model. A chi-squre test was conducted using Press’s Q calculated by [N-(n*K)]2/N(K-1) in order to determine if the discriminant model was statistically better than the chance model. The first discriminant model for ‘email’ was significant at p=0.001. The significant variables are: relative advantage, compatibility, computer self-efficacy, financial slack, innovativeness and image. Interestingly, ease of use and competitive pressure didn’t come up as significantly related to email adoption. The classificatory accuracy of the model was 86.4% and the t-test indicates that it is significantly better than the change model. The second for ‘business homepage was significant at p=0.017. The significant independents are relative advantage, compatibility, computer self-efficacy, image and competitive pressure. The classificatory ability of the discriminant model was 84.8% and the t-test indicates that it is better than the chance model. The third for e-sales was significant at p=0.015. Significant independent variables are relative advantage, compatibility, computer self-efficacy, financial slack, and image. The classificatory ability of the discriminant model was 72.7% and the t-test indicates that it is better than the chance model. The fourth model for e-procurement was presented in Table 7. The discriminant model for this technology was not significant at p