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EJISDC (2007) 31, 4, 1-15

BEHAVIORAL INFLUENCES ON E-COMMERCE ADOPTION IN A DEVELOPING COUNTRY CONTEXT

Faith-Michael E. Uzoka Information Systems Group

Department of Accounting and Finance University of Botswana, Botswana

[email protected]

Alice P. Shemi Information Systems Group

Department of Accounting and Finance University of Botswana, Botswana

[email protected] G. Seleka

Information Systems Group Department of Accounting and Finance

University of Botswana, Botswana [email protected]

ABSTRACT Electronic commerce (e-commerce) is rapidly gaining a prominent place in the global marketing matrix. The volume of transactions that are carried out over the Internet globally is extremely huge. It is estimated that in the next decade, e-commerce activities would be a major source of foreign exchange, and a key indicator of national development. Studies show that e-commerce development in developing countries, especially in Africa is comparatively very low. Infrastructural, economic, and management factors have been previously identified as contributing to the low level of e-commerce development in developing countries. This study focuses on behavioural factors in the adoption of e-commerce in developing countries. The results of the study show that perceived advantages, Internet and complexity, accessibility, and management support have statistically significant influence on the adoption of e-commerce, while perceived disadvantages and other facilitating conditions do not significantly affect the decision to adopt e-commerce. The study results tend to agree with the theory of planned behaviour, but attitude seems to weigh more than subjective norm and perceived behavioural control.

Keywords: E-commerce, Developing Countries, Behavioural Factors, Technology Diffusion, Planned Behaviour.

1. INTRODUCTION There has been significant research on electronic commerce, but majority of the studies have focused on the developed countries such as the United States of America, Canada, and Western Europe (Garcia-Murillo, 2004). However, most of the world’s population exists outside the borders of these countries. Little emphasis has been placed on studying the adoption and diffusion of e-commerce in developing countries. These countries tend to lack the infrastructural, economic, and socio-political framework for the development of electronic-commerce in comparison to developed countries. However, some developing countries have initiated strategic moves aimed at achieving an appropriate level of e-commerce development.

A number of studies have applied the theories of diffusion of innovation (Rogers, 1983) and of planned behaviour (Fishbein and Ajzen, 1975) to e-commerce adoption especially in developed countries. This study adds to the literature of the application of the

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theory of planned behaviour in developing countries by investigating behavioural factors affecting the adoption of e-commerce in Africa, using Botswana as case study. Attitudinal issues are also thought to play a significant role in e-commerce adoption, especially in a society like Botswana that is said to be xenophobic. Xenophobia could affect behaviour or attitudes of people towards something new and different (Campbell, 2003). Attitude is defined as an individual’s positive or negative feelings about performing target behaviour (Fishbein and Ajzen, 1975). It is related to behavioural intention because people form intentions to perform behaviours toward which they perceive to affect them positively. The diffusion of innovations theory (Rogers, 1983) suggest that the different dimensions of attitudinal belief toward an innovation can be measured using the five perceived attributes, namely; relative advantage, compatibility, complexity, trialability, and observability.

Botswana is one of the developing countries of Africa with a fair degree of e-readiness. A recent study (Ifinedo, 2005) shows that Botswana has an e-readiness of 2.47 on a five-point scale, only behind South Africa (2.78) and higher than the African mean of 2.22. Africa shows a very low level of e-readiness in comparison with other regions in the world, which stand as follows: USA – 4.36, G7 (Group of seven developed western nations) – 3.91, and East Asia – 2.99. The Global Competitive Report (World Economic Forum, 2007) has placed Botswana as the 8th most competitive African economy. However, not much study has been carried out on the level of e-commerce development and attitudinal factors that influence the adoption of e-commerce in Botswana. Majority of studies conducted on both e-commerce development and attitudinal factors that influence the adoption of e-commerce were in developed nations (Hawk, 2004), while predictions point to a significant growth in e-commerce in developing countries in the first decade of the twenty first century (McConnel, 2004).

In Section 2.0 we review some previous works related to e-commerce development especially in developing countries. Section 3.0 outlines the research framework, while the methodology of the study is shown in Section 4. The results of our analysis are presented and discussed in Section 5.0. In Section 6.0, we present some conclusions/policy implications.

2. REVIEW OF RELATED WORKS

The development of e-commerce is dependent on the development of websites as the websites are the main gateway to the Internet for any type of e-commerce. The evolution of the number of www servers worldwide is a useful indicator of the growth of e-business. As at June 2004 there were over 51,635,000 websites worldwide (I-Ways, 2005). It is suggested in (Gary et al., 2004) that a necessary precursor to the success of electronic commerce is active engagement of Internet activities by the population at large. Internet usage is well noticed in the developed countries such as USA, UK, Europe, etc but not much impact in the developing nations and more so Third World Countries (UNCTAD, 2004).

E-readiness is fundamental to the adoption of e-commerce. It represents the capability of nations to create, diffuse, adopt and use various components of the networked economy. The rankings of e-readiness survey have become an established benchmark for countries seeking to harness the Internet’s potential to drive business efficiency, improve the provision of public services and encourage the integration of local economies with the global economy (Lane et al., 2004). Popular e-readiness variables include: connectivity and technology infrastructure, business environment, consumer and business adoption, legal and policy environment, social and cultural infrastructure, and supporting e-services (Economist Intelligence Unit, 2006). It is noted (Mutula and Brakel, 2006) that e-readiness in developing countries, especially in Africa is low when compared with those of developed nations, which


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may account for the low development of e-commerce in developing countries. Dada (2006) carried out a study on e-readiness measures with respect to developing countries, and concluded that the measures tend to focus on the wider environment while ignoring the level of the organization. The study proposed a model that gives importance to both e-readiness (the environment) and technology acceptance (the organization). Molla and Licker (2005) proposed a model for e-readiness in developing countries based on perceived organizational e-readiness (POER) and perceived environmental e-readiness (PEER). The study found out that organizational factors have a greater influence on e-commerce adoption than environmental factors.

E-commerce development has been staged into three (UNCTAD, 2001) as follows:

i. Readiness: This is the earliest stage of e-commerce development, which deals with the readiness of people, businesses, infrastructure, and the economy as a whole for e-commerce activities.

ii. Intensity: This second stage deals with the intensity with which information and communications technologies are utilized within a country, and the extent to which electronic commerce activities are undertaken.

iii. Impact: This is the last stage of e-commerce development, at which time e-commerce begins to make impact on national economy and business activities in the country.

Most developing countries are either at the first (readiness) or second (intensity) stage of e-commerce development (UNCTAD, 2001). Several factors have contributed to the poor pace of e-commerce development in developing countries, especially in Africa. Such factors include: consumer mistrust of local Internet service and products (Pavlou, 2003); uneven diffusion of Internet across countries and poor ICT infrastructure (Rose and Straub, 2001), (Garcia-Murillo, 2004), (Dutta and Roy, 2004); unorganized electronic marketing (Rovenpor, 2003); government policies and low credit card penetration (Hawk, 2004). Trust and economic conditions explain more than 80% of variability in online shopping behaviour (Mahmood et al., 2004).Tarafdar and Vaidya (2004) studied the strategic and environmental imperatives of e-commerce adoption in India and identified three conditions under which an organization adopts e-commerce; namely: external environment, organizational performance and specific internal management compulsion. Jennex and Amoroso (2002) identified lack of a planning process for e-business applications, development and testing concerns, and branding issues among key inhibitors of electronic business, using Ukraine as a case study. Vatanasakdakul et al. (2004) studied e-commerce adoption in Thailand, and observed that immediate social and cultural expectations of e-commerce users are not met by e-commerce technologies, which evolved from the western society and designed to meet the needs that do not necessarily exist in most developing countries. This fact is supported by Kodakanachi et al. (2006) who proposed an economic development model for information technology (IT) in developing countries. The model includes: large foreign investments, government policies and support for IT, social awareness of IT importance, and efficient use of IT.

This study proposed another factor which is not highly researched in developing nations; the attitudinal and behavioural issues of the theory of planned behaviour. These are also thought to play a significant role in e-commerce adoption. Ajzen (Ajzen, 1991) states that an individuals’ intention to adopt an innovation is determined by three factors, attitude, subjective norms and perceived behavioural control. The diffusion of innovation theory proposed by (Rogers, 1983) provides five product or service categories that influence consumers’ acceptance of new products or service. The factors are: relative advantage, compatibility, complexity, trialability and risk. Rogers’ work has been applied in various studies in the USA and Australia: In the USA, one would find the works of (Tornatzky and


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Klein, 1982), (Moore and Benbasat, 1991), (Taylor and Todd, 1995), (Kolodinsky and Hogarth, 2001), (Kolodinsky and Hogarth, 2001), while (Sathye, 1999) stated that theory of planned behavior (TPB) has been successfully applied to various situations in predicting the performance of behavior and intentions, such as predicting user intentions to use a new software.

The decomposed TPB model uses constructs from the innovation literature (e.g., relative advantage, compatibility). It also explores subjective norms (e.g., social influence) and perceived behavioural control more completely by decomposing them into more specific dimensions (Tan and Teo, 2000). It provides a comprehensive way to understand how an individual’s attitude, subjective norms and perceived behavioural control can influence his or her intention to use electronic commerce. With reference to (Ajzen, 1991), we can assert that adoption and diffusion of electronic commerce by an individual consumer is determined by three factors. They are (1) attitude, which describes a person’s perception towards using e-commerce (2) subjective norms, which describe the social influence that may affect a person’s intention to use electronic commerce technologies, for example if a friend uses it, one may also use it; and (3) perceived behavioural control. Attitude is defined as an individual’s positive or negative feelings (evaluative affect) about performing target behaviour (Fishbein and Ajzen, 1975). It is related to behavioural intention because people form intentions to perform behaviours toward which they have positive affect. The diffusion of innovations theory suggests that the different dimensions of attitudinal belief toward an innovation can be measured using the five perceived attributes; namely: relative advantage, compatibility, complexity, trialability, and risk.

Tornatzky and Klein (1982) found relative advantage to be an important factor in determining adoption of new innovations. In general, perceived relative advantage of an innovation is positively related to its rate of adoption (Rogers, 1983). However, an innovation is more likely to be adopted when it is compatible with individuals’ job responsibilities and value system. Kaynak et al. (2005) studied factors affecting the adoption of e-commerce by SMEs in Turkey. The study indicates that e-commerce adoption is significantly influenced by its perceived usefulness, while the effect of perceived limitations was not statistically significant. Also, industry specific factors did not statistically impact on e-commerce adoption. Previous research has also indicated that an innovation with substantial complexity requires more technical skills and needs greater implementation and operational efforts to increase its chances of adoption (Cooper and Zmud, 1990). Rogers in (Tan and Teo, 2000) argues that potential adopters who are allowed to experiment with an innovation will feel more comfortable with the innovation and are more likely to adopt it. (Bauer, 1960) and (Ostlund, 1974) introduced risk as an additional dimension in diffusion and adoption. A common and widely recognized obstacle to electronic commerce adoption has been the lack of security and privacy over the Internet (Shemi and Magembe, 2002). This has led many to perceive electronic commerce as a risky undertaking.

While existing literature on technology diffusion and planned behaviour stress their importance in behavioural factors, it is noted that these theories recognize the existence of other facilitating factors, which in most cases, are existent (especially in the developed world). In the developing world, these facilitating factors are either lacking or not properly developed. This study focuses more on the relevance of behavioural factors as complementing to other factors in determining the intention to adopt e-commerce in a developing country context.


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3. RESEARCH FRAMEWORK Most innovation adoptions promote several dominant perspectives: managerial imperative, organizational imperative, technological imperative, and environmental imperative (Molla and Licker, 2005). Technological imperative models include the diffusion of innovation (DOI). The DOI model Rogers (Rogers, 1983) sees innovations as being communicated through certain channels over time and within a particular social system. The rate of adoption is impacted by five factors: relative advantage, compatibility, triabability, observability, and complexity. Similar to the diffusion of innovation model is the technology acceptance model (TAM) (Davis, 1989) which considers complexity, compatibility, relative advantage, ease of use, and usefulness as key in adoption adoption/acceptance of new technology. A similar theory is the theory of planned behavior (TPB) (Ajzen, 1991), which identifies three factors that influence consumers’ acceptance of new products; namely: attitude, subjective norms and perceived behavioural control. Managerial imperative models seek to explain innovation adoption based on the innovativeness attributes of managers, their commitment to innovation and their background (Hage and Dewar, 1973). Organizational models look at internal characteristics of organizations as key determinants of innovation (Damanpour, 1991). Environmental imperative models on the other hand, see external influences such as market pressure, inter-organizational relationships, institutional forces and socio-economic forces as determinants of innovation adoption (Munene, 1991).

TAM and TPB, both of which have strong behavioural elements, assume that when a person forms an intention to act, that they will be free to act without limitation. In the real world, there are many constraints such as limited ability, time constraints, environmental and organizational constraints, and unconscious habits which limit the freedom to act (Bagoss et al., 1992). A recent study (Venkatesh et al., 2003) produced the Unified Theory of Acceptance and Use of Technology (UTAUT), which attempts to improve on the predictive ability of other individual models by identifying communalities and capitalizing on the best aspects of each model. The models unified in UTAUT include: Theory of Reasoned Action (TRA), TAM, Motivational Model (MM), TPB, Model of PC Utilization, Innovation Diffusion Theory, and Social Cognitive Theory.

This study adopts the TPB in investigating the behavioural factors influencing e-commerce adoption. The components of TPB are attitude, subjective norms and behavioural control. The TPB model is presented in Figure 1.

Attitude

Perceived Behavioural Control

Intention to adopt

ecommerce

Adoption of ecommerce

Subjective Norm

Figure 1: Framework for the Adoption and Diffusion of E-commerce


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Based on the framework, the following hypotheses were proposed and empirically tested:

H1: Organizational intention to adopt e-commerce is positively affected by the advantages perceived to be deliverable from the adoption.

H2: The Internet and other technological complexities tend to impede on the ability of organizations in Botswana to adopt e-commerce.

H3: The perceived disadvantages associated with e-commerce impact negatively on the organization’s intention to adopt e-commerce.

H4: Internet accessibility tends to affect the ability of organizations in Botswana to adopt e-commerce.

H5: A good level management support would encourage individual’s intention to adopt e-commerce.

H6: Other facilitating conditions such as setup costs and human resource capital would impact on the organization’s ability to adopt e-commerce

4. METHODS

4.1 Survey Procedure The data collection instrument for this research is the questionnaire, which was administered to product/service organizations in both public and private sectors of Botswana. Various industries that have the potentials of e-commerce adoption in their businesses were included in the population of the study. They include among others, manufacturing, financial services, medical, agriculture, education, human services, mining, information and communications technology, and government. The sampling frame is organized according to job positions in the organization, which form sampling strata. The following strata are identified for the purpose of the study: top level management, middle level management, operational level management, technical staff, administrative staff, operational staff, others. Two hundred questionnaires were distributed to staff of fifty establishments, which were randomly sampled from the industrial cities of Botswana namely, Gaborone, Francistown, and Maun; with an average of four persons sampled from each organization. The essence of stratifying the establishment is to be able to get a good distribution of response from a cross section of the organization; thus, increasing the validity of the results. One hundred and twenty six questionnaires (63 % response rate) were properly filled and used for the purpose of analysis.

4.2 Measures The questionnaire consisted of two parts. The first part captured the sample characteristics such as respondent’s age, sex, type of organization, job classification, size of organization, and age of organization. The second part of the questionnaire measures twenty six variables identified as being relevant to the theory of planned behaviour. These experimental (independent) variables are shown in Appendix 1; while level of e-commerce adoption (ECA) constitutes the dependent variable. The adoption constructs were measured using multiple items, which were measured on a five point Likert type scale (ranging from 1=strongly disagree to 5=strongly agree).

4.3 Analysis Procedure The data analysis method involved two steps. The initial step was to assess the discriminant validity of the instrument used to measure the variables in the theoretical model using the multivariate technique of factor analysis by principal components. The purpose was to establish the variables in the questionnaire that measure constructs relating to the theoretical model. The factor analysis was carried out using SPSS 14.0. The method of rotation was the varimax with Kaiser Normalization. Six factors were extracted using the M-criterium method


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on the basis of a Social Science rule which states that only the variable with loading equal to or greater than 0.4 and percentage of variance greater than 1 should be considered meaningful and extracted for factor analysis (Uzoka and Akinyokun, 2005). The primary goal is to obtain some factors each of which would load on some variables affecting Internet adoption with a view to determining the impact of each variable on the adoption of ecommerce in Botswana. Multiple regression analysis was further carried out in order to test the hypotheses relating to the effects of the behavioural factors obtained in the exploratory factor analysis. The regression also serves to obtain the explanatory power of the factors on e-commerce adoption.

5. RESULTS AND DISCUSSION The data analysis for this study was carried out using SPSS 14.0. Table 1 shows the following sample characteristics: respondent’s sex, age, and position in the organization; age and size of organization. The results show that majority of the respondents were males (63.5%) whose modal age ranges between 25 and 39 years (63.5%), and who were mostly middle level management staff (24.6%). The organizations were mainly medium sized (46.0%). Most of the organizations sampled had existed for 5 and 19 years (total of 50%).

No. Percent Respondent’s Age <25 25-39 40-60 >60 Respondent’s Sex Male Female

Position Admin Staff Mid Level Mgmt Op. Level Mgmt Op. Staff Tech. Staff Top Level Mgmt Other Size of Organization Small Medium Large Uncertain

Age of Organization Uncertain <5 5 -9 10 – 19 20 – 29 30 – 40 > 40

20 80 24 2 80 46

19 31 19 16 17 16 8 32 58 32 4

3 31 32 22 23 14 1

15.9 63.5 19.0 1.6 63.5 36.5

15.1 24.6 15.1 12.7 13.5 12.7 6.3

25.4 46.0 25.4 3.2

2.4 24.6 25.4 17.5 18.3 11.1 0.8

Table 1: Sample Characteristics Exploratory factor analysis was carried out in order to attempt to factor the

experimental variables into factors. Principal component analysis was utilized in the analysis, while the rotation method was the varimax method with Kaiser Normalization. The Bartlett’s test produces a χ2 of 1434.978 [>>500] with a significance level of 5.477E-139 [<<0.05], which shows that the sample taken from the total population under study is adequate. The


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KMO test produces a measure of 0.781[>0.5], which further confirms the adequacy of the sample. The results obtained from the Bartlett’s test and KMO test also indicate the suitability of the application of factor analysis. The results of the factor analysis are shown in Table 2, which shows the rotated component matrix with the loadings that are extracted and considered relevant to the constructs highlighted in bold. The cut-off for loadings was 0.4. The exploratory factor analysis shows that twenty four out of the twenty six variables loaded on six distinct factors, accounting for a total of 61.392% of the variance in the data. Two variables were dropped in the analysis because they exhibited cross loading. The Cronbach’s alphas for the factors indicate good reliability values [α > 0.5].

Component PercAdv IntCompx PercDisad Access MgtSupt FacCond EBR .842 .016 -.004 .142 -.040 .154 AIM .832 .176 .010 .056 -.007 -.099 ANM .772 .287 .259 .056 -.109 .114 CMR .771 .178 .024 .038 .039 .015 QRC .745 .302 -.054 .131 .093 .063 ICS .660 -.080 .113 .024 .039 .316 COI .507 .047 .065 -.208 .385 .211 LKE .053 .812 -.047 -.040 -.136 .050 WDS .228 .674 .098 -.078 .020 -.197 WDP .129 .658 -.190 .038 .268 -.015 CDN .198 .580 -.027 .110 -.078 .148 CLI .178 .567 .174 -.167 -.118 .334 HCO .038 .002 .764 .128 .115 -.077 MRN .134 -.109 .735 -.155 .339 .073 ISI .008 .192 .627 .238 .342 .184 DAI .101 -.126 .603 .321 .272 .110 CAS .028 .049 .597 .355 .031 .272 CCL .109 .044 .066 .816 .092 -.013 CAI .174 -.088 .142 .768 .208 .129 TRI -.033 -.064 .249 .436 .395 .018 SMC -.024 -.041 .298 .262 .858 .003 LFD .264 .240 -.056 -.007 .713 .377 LSC .008 -.033 .252 .321 .084 .776 HRS -.051 -.079 .379 .354 .313 .632 Eigenvalues 6.190 4.341 1.891 1.483 1.288 1.140 % Variance 16.906 11.486 8.768 8.684 7.81 7.737 Cumm. % Variance

16.906 28.392 37.16 45.844 53.655 61.392

Cronbach’s α 0.8893 0.7792 0.694 0.7606 0.7274 0.5965 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. A Rotation Converged in 8 Iterations.

Table 2: Rotated Component Matrix Given below, are the six factors and variables that load on them.


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Perceived Advantages of Ecommerce: Improved and expanded customer service (ICS), expanded business reach (EBR), access to international consumer markets (AIM), quicker response to changes in the market (QRC), a new means of collecting market research data (CMR), low running costs for the operation of the Internet (COI), increased access to niche consumer markets (ANM)

Internet and Complexity Factor: Web design skills of company personnel. (WDS), costs of development and computer networking technologies (CDN), limited knowledge of e-commerce models and methodologies (LKE), company’s logistical infrastructure (CLI), web developer’s promotional offers (WDP).

Perceived disadvantages of E-commerce: Concerns about online security (CAS), Internet’s inability to convey sensual information (ISI), data integrity (DAI), and media reporting of the negative aspects of the Internet (MRN).

Accessibility Factor: Company’s target customers’ levels of access to the Internet (CAI), company’s target customers’ levels of computer literacy and Internet awareness (CCL), and technical reliability of the Internet (TRI)

Management Support: Level of funding available for retail development on the Internet (LFD), senior management’s level of commitment to e-commerce (SMC)

Other Facilitating Conditions Factor: Level of human resources available (HRS), and low set up costs of on-line operation (LSC)

The factors show some level of agreement with the general factors proposed in the theory of planned behaviour. The total variance explained by the extracted factors is 61.392 % as shown in Table 2. The remaining 38.608% variation is accounted for by extraneous factors. Among the extracted factors, perceived advantages of e-commerce, and Internet and complexity factor account for a considerable degree of variance (16.906% and 11.486% respectively). The Internet and complexity factor impacts greatly on the level of e-commerce adoption because of the low level of computer literacy and the inadequacy of available human capital to operate Internet and e-commerce facilities.

The results of the multiple linear regression analysis are presented in Tables 3-5. Table 3 shows the summary statistics, which shows adjusted R2 of0.305 and a standard error of 0.834. A further ANOVA test (Table 4) was carried out on the regression results in order to determine whether the association between the dependent variable (eca) and the combined factors is statistically significant. An F value of 10.137 and a significance F of 0.000 was generated from the ANOVA test. Thus the null hypothesis is rejected, implying that there is a significant relationship between the independent variables and the dependent variable (e-commerce adoption).

R R Square Adjusted R Square Std. Error of the Estimate Model

1 .582 .338 .305 .83444 a Predictors: (Constant), FacCond , MgtSupt , Access , PercDisad , IntCompx , PercAdv

Table 3: Model Summary

Model Sum of Squares

Df Mean Square

F Sig.

1 Regression 42.349 6 7.058 10.137 .000 Residual 82.859 119 .696 Total 125.209 125

a Predictors: (Constant), FacCond , MgtSupt , Access , PercDisad , IntCompx , PercAdv b Dependent Variable: ECA

Table 4: ANOVA


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Table 5 shows the regression coefficients. The t-values indicate that perceived advantages, Internet and complexity, accessibility, and management support have statistically significant predictive capability; which implies that they exert a significant influence on the firm’s ability and decision to adopt e-commerce [|t| ≥ 2]. The perceived disadvantages and other facilitating conditions are not statistically significant in a firm’s decision to adopt e-commerce. H1, H2, H4, and H5 are supported, while H3 and H6 are not supported. The regression coefficients show that management support factor exert the highest significant influence (t=5.471, p=.000) followed by Internet and complexity factor (t=3.501, p=.001) and the Internet access factor (t= 3.333, p=.001).

Unstandardized Coefficients

Standardized Coefficients

t Sig.

Model B Beta 1 (Constant) 2.824 52.535 .000 PercAdv .175 .174 2.340 .021 IntCompx .261 .261 3.501 .001 PercDisad -7.998E-02 -.080 -1.072 .286 Access .249 .249 3.333 .001 MgtSupt .408 .408 5.471 .000 FacCond -7.086E-02 -.071 -.949 .344 a Dependent Variable: ECA

Table 5: Regression Coefficients The results show that perceived advantages, Internet complexity, access and

managerial support affect e-commerce adoption. These are mainly organizational, cost and infrastructural issues. This is consistent with previous findings by (Looi, 2005) and (Molla and Licker, 2005). (Molla and Licker, 2005) found that at the initial stage of e-commerce adoption, organisational and environmental factors affect e-commerce adoption in developing countries. It concludes that human, business, and technology resource dimensions of organisational e-readiness have a major effect on initial e-commerce adoption in developing countries. These are referred to as ‘initial generation factors’ as they usually appear at the initial stage of e-commerce adoption. Results of this study generally reinforce the idea that these factors influence e-commerce adoption in Botswana.

The β coefficients show that ‘perceived disadvantages’ such as Internet’s inability to convey sensual information, technical reliability of the Internet, quicker response to changes in the market, e-commerce as a new means of collecting market research data, increased access to niche consumer markets, and data integrity issues impact minimally on the decision to adopt e-commerce. This is supported by (Cooper and Zmud, 1990) who have classified the IT adoption process into six stages namely; initiation, adoption, adaptation, acceptance, routinization, and infusion. According to this classification, these factors seem to fit in the higher stages of the classification but not necessarily the first stage where Botswana’s e-commerce adoption resides. Equally, ‘other facilitating conditions’ such as low set up costs of online operation seem to have a negative influence on the adoption of e-commerce. The respondents believe that the set up costs for online operations are high. This factor is related to literature on the barriers of technology adoption (Aguila-Obra and Padilla-Melendez, 2006). This is supported by findings that argue that costs of setting up e-commerce infrastructure particularly in developing countries in Africa are generally higher than those in developed countries such as USA, UK and Europe.

The factors being considered are beyond Botswana’s e-commerce level of adoption and thus they confirm a minimal impact on the decision to adopt e-commerce. Furthermore,


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these issues are behavioural in nature as they portray some of the perceived advantages and disadvantages of the Internet/e-commerce. The implication is that the theories of planned behaviour may not be fully relevant in developing nations, such as Botswana. This could be explained by the fact that key infrastructural and technological capital, which are fundamental to the adoption of e-commerce are still not fully developed in developing countries. The low level availability of such facilities would mask behavioural considerations in the decision to adopt e-commerce.

6. CONCLUSIONS AND POLICY IMPLICATIONS The global economy has greatly been influenced by the introduction and adoption of e-commerce by businesses. Since the beginning of the twenty first century, the level of e-commerce activities has increased considerably. This research set out to determine the influence of behavioural factors that impact on the development of e-commerce in developing countries. Botswana was used as a case study, being one of the countries in Africa with a good degree of e-readiness and one of Africa’s best performing economy (World Economic Forum, 2007). The theory of planned behaviour (Molla and Licker, 2005) was applied in studying the development of e-commerce in a developing country context. The results of the study show that perceived advantages, Internet and complexity factor, accessibility factor, and management support have statistically significant impact on e-commerce adoption, while perceived disadvantages and other facilitating conditions have negative impacts that are not statistically significant.

The study is a positive step towards validating the theory of planned behaviour in a developing country context and also improving understanding on B2C ecommerce adoption in Botswana. In order to improve on the level of e-commerce development in developing countries, strong attention should be paid to infrastructural issues, especially in the rural communities where the a good percentage of the population reside. Government could encourage increase in Internet access, especially at the rural areas by providing enabling tax allowances for Internet Service Providers (ISPs) who are interested in developing Internet infrastructure in the rural areas. E-commerce thrives on the utilization of credit cards and other online payment systems. Cash driven economies like Botswana need to develop a system of participation in the international e-market place by encouraging the establishment of credit management firms within their economies. It is also noted that though not statistically significant, perceived disadvantages of e-commerce seem to impact negatively on the development of e-commerce.

These findings have a practical implication to both organizations and consumers alike. There is need to educate organizations and sensitize the consumers on the benefits of ecommerce. Organizations such as Botswana Chamber of Commerce and Industry (BOCCIM) could take a leading role in practically training its members on what ecommerce has to offer (Shemi and Magembe, 2002). Some of the training sessions could showcase demonstrations on video or broadcast on television for their member organizations and the public. This is important because at the level of intensity of development, it is believed that behavioural issues would play a significant role in the adoption of e-commerce.

Another critical area that needs pragmatism is the regulatory framework for ecommerce in Botswana. This needs to be made more available and be aligned to acceptable world standards for ecommerce activity to increase in Botswana. Government and BOCCIM can help accelerate B2C ecommerce adoption by selecting the best information, synthesizing it, and disseminating it to the stakeholders (Moodley et al., 2003) through print and electronic media. Realizing the potential utilization of ICT in accelerating development in the country,


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the government of Botswana, through the Ministry of Communication, Science and Technology has spearheaded the development of a National ICT policy that proposes the creation of electronic trading hubs to enable rural communities to efficiently trade with larger consumers in the urban areas. Since B2C seems to be more of a local phenomenon (Gibbs et al., 2002) in contrast to B2B which is globally driven, B2C which is “pulled” by consumer markets that are mainly local and therefore divergent; it may be possible to derive practical solutions from all stakeholders in the country. Other practical aspects that have been mentioned in the national ICT policy and need revising are:-measures to protect online customers from fraud, including ecommerce laws ensuring legality of online contracts and transactions; changes to banking laws that will be necessary to ensure that credit card transactions and foreign currency transactions are enacted and the deregulation of the telecommunications industry.

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8. APPENDIX 1–EXPERIMENTAL VARIABLES Dependent Variable: Level of E-commerce adoption (ECA) Independent Variables: a. Level of funding available for retail development on the Internet (LFD) b. Senior management’s level of commitment to e-commerce (SMC) c. Level of human resources available (HRS) d. Web design skills of company personnel. (WDS) e. Concerns about security aspects (CSA) f. Costs of development and computer networking technologies (CDN) g. Limited knowledge of e-commerce models and methodologies (LKE) h. Company’s logistical infrastructure (CLI) i. Company’s target customers’ levels of access to the Internet (CAI) j. Company’s target customers’ levels of computer literacy and Internet awareness (CCL) k. Technical reliability of the Internet (TRI) l. Web developer’s promotional offers (WDP) m. Low set up costs of on-line operation (LSC) n. Improved information exchange with customers (IIE) o. Improved and expanded customer service (ICS) p. Expanded business reach (EBR) q. Access to international consumer markets (AIM) r. Quicker response to changes in the market (QRC) s. A new means of collecting Market Research Data (CMR) t. Low running costs for the operation of the Internet (COI) u. Increased access to niche consumer markets (ANM) v. High cost of running on-line and off-line operation. (HCO) w. Concerns about on-line security (CAS) x. Media reporting of the negative aspects of the Internet (MRN) y. Internet’s inability to convey sensual information (ISI) z. Data Integrity (DAI)


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