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African Journal of Science, Technology, Innovation and Development

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Factors influencing the implementation of e- commerce innovations: The case of the Nigerian informal sector

B. F. Ajao, T. O. Oyebisi & H. O. Aderemi

To cite this article: B. F. Ajao, T. O. Oyebisi & H. O. Aderemi (2018) Factors influencing the implementation of e-commerce innovations: The case of the Nigerian informal sector, African Journal of Science, Technology, Innovation and Development, 10:4, 473-481, DOI: 10.1080/20421338.2018.1475541

To link to this article: https://doi.org/10.1080/20421338.2018.1475541

Published online: 18 Jun 2018.

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Factors influencing the implementation of e-commerce innovations: The case of the Nigerian informal sector

B. F. Ajao1*, T. O. Oyebisi2 and H. O. Aderemi 3

1National Centre for Technology Management, Obafemi Awolowo University, Ile-Ife, Nigeria 2African Institute for Science Policy and Innovation, Obafemi Awolowo University, Ile-Ife, Nigeria 3Department of Management and Accounting, Obafemi Awolowo University, Ile-Ife, Nigeria *Corresponding author emails: [email protected]; [email protected]

This paper investigated the factors influencing e-commerce adoption amongst small enterprises in Southwestern Nigeria with a view to contributing to debates on technological adoption in the informal sector.

Primary data were collected from 387 enterprises engaged in furniture works, leather products, clothing and textiles through the use of questionnaire that were administered using multistage and simple random sampling technique. Data were analysed using logistic regression.

The result showed that the significant technological factors influencing e-commerce adoption were the number of employees with ICT background (r = 0.654; p < 0.05), accessibility to good quality Internet bandwidth (r = 0.826; p < 0.05) and the availability of Internet security technologies (r = 0.809; p < 0.05) amongst others. The significant non- technological factors were basically e-readiness of customer and supplier for e-commerce (r = 0.612; p < 0.05), government policies (r = 0.684; p < 0.05) and the attitude of the owner of the enterprise towards e-commerce adoption (r = 0.598; p < 0.05). In addition, the non- technological factors had different influences on adoption when the enterprises were analysed by sector.

The study concluded that factors influencing e-commerce adoption differs across the different categories of business in the informal sector; hence, there is the need for individual reassessment when designing policy towards technological adoption amongst small enterprises.

Keywords: small enterprises, e-commerce adoption, technological factors, non-technological factors, Nigeria

Background The concept of globalization has brought changes in global poverty, and the need for development assistance has made prominent the subject of e-commerce (Kanbur and Sumner 2012). This implies that anywhere in the globe, a firm or individual can be reached and patronized. However desirable this is, it has remained far-fetched and unrealistic for some firms in developing economies to have web presences, let alone to implement integrated e- commerce (Aderemi 2015). Although, e-commerce has been embraced to a large extent by some multinational and large organizations and there are studies on the adop- tion of e-commerce by small and medium enterprises (SME) (Gilaninia et al. 2011), developing countries have always been below the frontier in technological adoption and thus have invariably been a bit backward and less competitive as compared to the advanced countries (Bekele and Muchie 2009).

The informal sector has been discovered to be very large, generating significant social and economic benefits, and to include a significant portion of the population in developing countries. Studying innovation in the informal sector helps in an understanding of the role innovation plays in inclusive development (Jegede and Ojo 2012). The sector comprises 48%, 51%, 65% and 72% of non- agricultural employment in North Africa, Latin America, Asia and Sub-Saharan Africa respectively. It also accounts for 70% of GDP and 95% of total employment in middle income countries, over 60% of GDP and over 70% of total employment in low income countries (ILO 2002). Accord- ing to National Bureau of Statistics (2013), about 99.87% of all businesses in Nigeria are in the informal sector and

accounted for 35.5% of GDP. All of these statistics shows that the sector plays a key role in national development and needs to be studied thoroughly in all its ramifications. E-commerce will not stand aloof in this regard as it is known to be one of the propellers of small enterprises in other countries (Jeffcoate, Chappell, and Feindt 2002).

The broad objective of this paper is to appraise the adoption of e-commerce among small and micro enter- prises in Nigeria with a view to providing information that would enhance practicability and competitiveness among entrepreneurs. Its specific objective is to investi- gate the technological and non-technological factors influ- encing e-commerce adoption among small scale enterprises in Southwestern Nigeria.

Literature review Studies on e-commerce adoption have used one or a com- bination of theories such as Theory of Reasoned Action (TRA) by Fishbein and Ajzen (1975), Theory of Planned Behavior (TPB) by Ajzen (1991), Technology Acceptance Model (TAM) (Davis 1989), Diffusion of Innovations and Innovation Diffusion Theory (Rogers 1995, 2003) and Institutional Theory (Scott and Christen- sen 1995), among others. TRA was based on the assump- tion that human beings are rational and that they make systematic use of available information; and they consider the implications of their actions before making the decision to perform or not to perform a given behaviour. TPB asserted that the likelihood of performing a given be- haviour will be strong if the individuals hold a favourable attitude towards the performance of that behavior. The

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African Journal of Science, Technology, Innovation and Development, 2018 Vol. 10, No. 4, 473–481, https://doi.org/10.1080/20421338.2018.1475541 © 2018 African Journal of Science, Technology, Innovation and Development

behaviour referred to in this paper is e-commerce. TAM emphasized the perceived usefulness and the perceived ease of use of a technology as the determinants for adop- tion. In order to incorporate additional theoretical con- structs which TAM originally lacked, such as social influence processes (subjective norm, voluntaries, image and experience) and cognitive instrumental processes (job relevance, output quality and result demonstrability), Enhanced Technology Acceptance Model (TAM2) was developed by Venkatesh and Davis (2000). TAM2 incor- porates social influences and subjective norm into an indi- vidual’s perceptions of usefulness and this gave more avenues for more factors to be investigated. These additional theoretical constructs, which the earlier men- tioned theories do not have, is the justification for employ- ing TAM2 for this paper.

Studies have been conducted relating to the adoption of e-commerce technologies around the world, but most of these were concentrated in relatively well-developed economies such as United States (Grandon and Pearson 2004), New Zealand (Al-Qirim 2007), Denmark and Aus- tralia (Scupola 2009). Only a few studies have been under- taken in Africa. In Kenya, Ochola (2013) examined the various determinants of slow e-commerce adoption against organizational, environmental and technological factors. In Botswana, Lawrence and Tar (2010) discovered that the patterns of e-commerce adoption are dependent on the nature and characteristics of the SMEs. The literature in Nigeria, for example Uzoka (2008) worked on organiz- ational influence on e-commerce adoption, while

Chiemeke and Evwiekpaefe (2011) dwelt on the frame- work of a modified unified theory of acceptance and use of technology model with Nigerian factors in e-commerce adoption.

The gap in the literature is that no studies have exam- ined factors influencing e-commerce adoption among small enterprises that are focused on local products especially in the area of leather, clothing and furniture works in Nigeria. This study was carried out in order to investigate the factors influencing the use e-commerce as a promoter of locally made products. Naturally, it would be expected that all informal enterprises should have same the behaviour as SMES but it would also be wrong to generalize because the enterprises selected for this paper already have two peculiarities. Not only are they informal but the nature and the characteristics of their business also matters as shown by Lawrence and Tar (2010). The enterprises selected for this study are of a different nature; hence, they must be studied differently. E-commerce research activities in Nigeria offer a narrow understanding of the factors influencing its adoption as most studies on e-commerce tend to make generalization that the same factors are affect all SMEs, not taking into cognizance the peculiarity in the type of product or nature of the business involved (Ayo, Adewoye, and Oni 2011; Kinuthia and Akinnusi 2014).

Figure 1 shows the proposed conceptual framework that illustrates the relationships between e-commerce adoption, factors influencing its adoption, the e-payment platform used and its effect on firm’s performance. It

Figure 1: Conceptual framework for e-commerce adoption among small enterprises.

474 Ajao, Oyebisi, and Aderemi

shows that e-commerce adoption is dependent on techno- logical and non-technological factors; e-payment and firm performance are the independent variables. However, the focus of this paper is on the technological and non-techno- logical factors. The framework shows the relationship between technological and non-technological factors and e-commerce adoption in such a way that if any of the factors hinder the adoption of e-commerce, then there is a need to investigate the reason why it hinders it and rec- ommend policy that would help adjust the factors posi- tively. Similarly, if any of the factors promote e- commerce adoption, then the variables for such factors should be enhanced with the right policy advice.

Research methodology The study covered three states in Southwestern Nigeria: Lagos, Oyo and Ogun state. Nigeria has 36 states and six geopolitical zones. The rationale for choosing the Southwestern zone is because the zone has the highest concentration of small-scale enterprises, about 36.9% of the national total (SMEDAN 2013). In addition to the aforementioned criteria, the Southwestern zone is the com- mercial hub of Nigeria and is recognized as the region with the lowest poverty indices (NBS, 2013). It is therefore necessary to study the region as a model to examine how e-commerce adoption has helped in contributing to these low poverty indices.

The population for the study consists of all small enter- prises in the study area; Thus, over 10,000 firms formed the target population for the study (SMEDAN and NBS 2013).

In order to get a sample size that is appropriate for the study, Mugenda’s (2013) formula was used to estimate the sample size:

n = Z 2 pq

d2 (1)

where

n is the desired sample size if the target population >10,000. (The population of manufacturing SSEs in the Southwestern region of Nigeria is more than 10,000 (SMEDAN and NBS 2013).

Z is the standard normal deviate at the required confidence level. Confidence level at 95% (standard value of 1.96).

p is the proportion in the target population estimated to have the characteristic (assume 50% if unknown): q = 1–p.

d is the level of statistical significance or α = 0.05.

n = 1.96 2∗0.5∗0.5 0.052

n = 384

At the 95% confidence level, 384 enterprises would be suitable for the study. Three business hubs with high con- centrations of enterprises that relate to the study area were purposely selected in each of the three states This resulted

in a total of 387 enterprises: 129 enterprises from each of the three state’s capitals drawn from the furniture (43), leather (43), clothing and textile (43) enterprises. The sole proprietors were purposively selected as the respon- dents for the study.

Model specification Logit regression was adopted to test the impact of the indi- vidual factors on the dependent variable. This was con- sidered appropriate because the dependent variable (e- commerce adoption) was dichotomous. Binary logit models involve dichotomous dependent variables whose probabilities, conditional upon explanatory variables were modelled. Since there could be two choices such as whether the firms adopt e-commerce or not, a simple logit model was relevant. A binary regression with (0, 1) choice was represented as

y∗i = b′Xi + 1i (2)

where y∗i was a latent variable and β′ was the coefficient of explanatory variables Xi. The latent variable y

∗ i was not

directly observable; rather, what was observable, was a dummy variable yi which represented whether the firms adopted e-commerce (i.e. yi = 1) or did not adopt it (i.e. yi = 0). The coefficients of the logit model like the ordinary regression coefficient defined the parameter estimates. These coefficients signified that a unit increase in the inde- pendent variable (Xt) produced βt change in the log odds of the dependent variable. A positive sign for the coeffi- cients indicated that the log of the odds ratio of the depen- dent variable increased as the value of the independent variable was raised and vice versa.

The equation for the technological factor was:

Log(odds) = ln p1 + p2 + . . . + pj−1 1 − p1 − p2 − . . . − pj−1

= Logit Yi = b0 + b1X1 + b2X2 + . . . + bkXk (3)

Y1 β0 + β1X1 + β2X2 + β3X3 + … β9X9 + ε X1 Number of employee with ICT background X2 ICT units in the firm X3 Training X4 Frequency of training X5 Existing technologies X6 Internet bandwidth X7 Quality of internet service X8 Internet security technology

The model measured the significant impact and the direction of influence of the aforementioned independent variables on the dependent variable.

Linear regression was also used to establish the com- binational influence of all the independent variables on the dependent variable. The equation for the model is:

Y2 = b0 + b10X10 + b11X11 + b12X12 + . . . b15X15 + 1 (4)

African Journal of Science, Technology, Innovation and Development 475

where

Y is the dependent variable, that is, e-commerce adoption. β0 is the constant or intercept. β9–β15 are the regression coefficients or change induced in

Y by each X. ε is the error term.

The model that was used to access non-technological factors is

Log(odds) = ln p1 + p2 + . . . + pj−1 1 − p1 − p2 − . . . − pj−1

= Logit Y2 = b0 + b1X1 + b2X2 + . . . + bkXk (5)

where

Y2 E-commerce adoption among small enterprises (depen- dent variable)

1 Adoption of e-commerce (Yes) 0 No adoption of e-commerce (No) X9 Readiness of customer and supplier to conduct business

electronically X10 Size of customer X11 Industrial support X12 Competition in the industry X13 Infrastructural support X14 Government policies X15 Attitude of the owner of the enterprise

Table 1 shows the technological and non-technological factors that were investigated and how they were measured.

Results and discussion Characteristics of the respondents Table 2 shows that the average response rate for the study was 89.9% as 348 questionnaires were retrieved from the 387 that were administered. The demographic character- istic of the respondents as shown in Table 3 reveals that 60.6% of the respondents are male. Also, majority of the respondents (53.7%) were within 41–50 years. On edu- cational qualification, the level of education and literacy was recorded to be very high among the SSEs owners; the majority (90.6%) had above primary education which indicates that they can probably read and write. Also, about 51.5% of the SSEs owners had attended one higher institution or the other while 6.3% were post-gradu- ate degree holders. The proportion of those without any form of education was only 3.7%. This is relatively small but significant because it means that some of the enterprises surveyed were managed by people who lacked the minimum ability to either read nor write. Firm size was measured in terms of the numbers of employees in the firm in line with SMEDAN (2013) that declared that employment-based classification overrides asset criteria because inflation may compromise the asset-based definition. In addition, the majority (95.1%) of the firms had below 10 employees. This means that the enterprises examined in this study were mainly microenterprises.

Table 1: Variables and indicators.

Variable Description Measure Technological factors influencing e-commerce adoption amongst small enterprises

Number of employee with ICT background/ICT devices

Employees with ICT background and if they have ICT unit

Method of ranking using frequency distribution

Training How well their employees attend training on the use of Internet and web technologies

1 if Yes; 0 if No Always = 3, occasionally = 2 and rarely = 1

Compatibility How fit their existing systems are to integrate e- commerce

Method of ranking using frequency distribution

Internet accessibility/ Bandwidth

Reliable access to fast and affordable bandwidth; quality of Internet service

1 if Yes; 0 if No No effect = 1, Minor effect = 2, Neutral = 3, Moderate effect = 4 and Major effect = 5

Infrastructural support Availability of electricity, good and affordable Internet supply, amongst other

Method of ranking using frequency distribution

Non-technological factors influencing the adoption of e-commerce innovation Attitude of CEO Disposition of the owner towards e-commerce adoption Not important = 1, low importance = 2,

average importance = 3, of high importance = 4 and essential = 5

Industrial support The level of industrial support towards e-commerce adoption

Not important = 1, low importance = 2, average importance = 3, of high importance = 4 and essential = 5

Competition in the industry How important competition is in determining the adoption of e-commerce

Not important = 1, low importance = 2, average importance = 3, of high importance = 4 and essential = 5

Customer and supplier e- readiness

Preparedness of the customer and supplier to conduct business electronically

Not important = 1, low importance = 2, average importance = 3, of high importance = 4 and essential= 5

Internet security Internet security tool or tools that they use in protecting their local network. The lists given were anti-virus, anti- spam filters, firewalls

Not important = 1, low importance = 2, average importance = 3, of high importance = 4 and essential = 5

476 Ajao, Oyebisi, and Aderemi

Technological factors The correlation analysis in Table 4 shows the relationship between e-commerce adoption and the technological factors that were examined. The result show that there was a strong positive correlation between e-commerce adoption and number of employees with ICT background (r = 0.654; p < 0.05), availability of good quality Internet (r = 0.826; p < 0.05), and Internet security technologies (r = 0.809; p < 0.05), amongst others. All the technological factors examined showed a positive adoption relationship with e-commerce adoption. However, there is a need to look at the impact of all the factors individually.

Table 5 shows that the exponential (B) value associ- ated with number of employee with ICT background, number of computer devices in each enterprise, frequency of training and compatibility with existing technologies are (0.459), (0.352) (0.709) and (0.454), respectively. Hence, when these variables are raised by one unit they are less likely to influence e-commerce adoption. On the other hand, employee with ICT background, training, affordable Internet bandwidth, quality of Internet service and Internet security technologies with exponential (B) value of (33.348), (4.886), (130.831), (10.412) and (23.903), respectively are likely to influence e-commerce adoption.

In real scenario, Wald statistic and associated probabil- ities provide an index of the significance of each predictor in the equation. The simplest way to assess Wald is to take

the significance values and if less than 0.05 reject the null hypothesis as the variable does make a significant contri- bution. In this case, at the 95% confidence level, only employee with ICT background (p = 0.000), affordable Internet bandwidth (p = 0.000), quality of Internet service (p = 0.000) and Internet security technologies (p = 0.000) are significant to the prediction at 5%. The result can be interpreted as indicating that firms that employee staff with an ICT background are more likely to adopt e-commerce. In fact, firms with only one staff member with an ICT background were capable of imple- menting e-commerce. Thus, in terms of human resource requirements, the number of employees with an ICT back- ground did not have a significant impact on adoption of e- commerce.

The findings of this study support the study of Aderemi et al. (2011) that revealed that a science and tech- nology workforce is important for high-tech firms. In addition, this study asserts that even one workforce member with the requisite knowledge in the area of the technology is sufficient for SSEs to adopt e-commerce. The only constraint may be that e-commerce adoption at that level may only be at initialization stage and not at the institutionalization stage. Table 5 reveals that the more firms have access to affordable Internet bandwidth and access to good quality Internet service, the more likely they are to adopt e-commerce. This buttresses the findings of Kabanda (2013) which revealed that high quality and affordable Internet service is essential for firms that want to integrate e-commerce in their business.

This paper also asserts that affordable and fast Internet facilities are one of the major prerequisites for e-commerce adoption. Most firms are of the opinion that Internet ser- vices are not readily available at a low cost and, in cases when they are, the quality offered by the service provider is not very good, thereby discouraging the use of e-com- merce application. In addition, the result showed that the

Table 2: Numbers of questionnaires administered and retrieved.

Respondents’ category

Questionnaire

Response rate (%)Administered Retrieved Furniture works 129 104 80.6 Leather works 129 123 95.3 Clothing and textile 129 121 93.8 Total 387 348 89.9

Table 3: Demographic characteristics of enterprises.

Industry Percentages (%) Gender

Female 39.4 Male 60.6 Total 100

Age 20–30 2.9 31–40 25.0 41–50 53.7 Above 50 18.4 Total 100

Educational qualification None 3.7 Primary 5.7 High school 32.8 OND/NCE 39.4 Bachelor 12.1 Post-graduate 6.3 Total 100

Numbers of employee Below 10 95.1 Above 9 4.9 Total 100

Table 4: Correlation between technological factors and e- commerce adoption.

Technological factors R Sig Number of employees with ICT background

0.654 0.000

Number of computer devices in your enterprise

0.530 0.000

Training 0.406 0.003 Frequency of training 0.205 0.018 Existing technologies 0.695 0.000 Internet bandwidth 0.764 0.000 Quality of internet service 0.826 0.000 Internet security technologies 0.809 0.000

African Journal of Science, Technology, Innovation and Development 477

more firms have Internet security tools, the more likely they are to adopt e-commerce. The model’s chi square (χ2) has 8 degrees of freedom, a value of 310.944 and a probability of p < 0.05. Thus, the indication is that the model fits well. The model summary shows that Nagelk- erke’s R2 is 0.805, indicating a very strong relationship of 81% between the predictors and the prediction. This can be translated to mean that the model accounts for 80% of the variance in the variable.

Non-technological factors Table 6 shows the correlation between e-commerce adop- tion and variables indicating the non-technological factors investigated. The results show that e-readiness of customer and supplier (r = 0.612; p < 0.01), size of customer (r = 0.582; p < 0.01), industrial support (r = −0.579; p < 0.01), competition in the industry (r = −0.566; p < 0.01), infrastructural support (r = −0.655; p < 0.01), governmen- tal policies (r = 0.684; p < 0.01) and attitude of the owner of the enterprise (r = 0.598; p < 0.01) were all significantly associated with the adoption of e-commerce, although only e-readiness of customer and supplier and attitude of the owner were in the positive direction.

In order to test the impact of these individual non-tech- nological factors on e-commerce adoption, logit regression was used. The result of this finding, in Table 7, shows that the exponential (B) value associated with size of customer, industrial support, competition in the industry and infrastructural support were (0.914), (1.498), (0.975) and (0.741), respectively. Hence, when these variables are raised by one unit, they are less likely to influence e-commerce adoption. On the other hand, readiness of customer and supplier to conduct business electronically, government policies and attitude of the owner of the enterprise with exponential (B) value of (11.989), (3.601) and (2.768), respectively are more likely to influence e-commerce adoption.

In real scenario, Wald statistic and associated probabil- ities provide an index of the significance of each predictor in the equation. In this case, at the 95% confidence level, only readiness of customer and supplier to conduct business electronically (exp B = 11.989; p < 0.05), (exp B = 3.601; p < 0.05) and attitude of the owner of the enter- prise towards e-commerce (exp B = 2.768; p < 0.05) were significant to the prediction. This is quite understandable because e-commerce is a technology and we are investi- gating non-technological factors, which may explain why most of the non-technological factors investigated were not significant and did not have a high impact on adoption. The result showed that the readiness of customer and supplier to conduct business electronically and the attitude of the owner of the enterprise toward e-commerce adoption will likely influence e-commerce adoption. This can be further interpreted that as the e-readiness of custo- mers and suppliers moved from not important to essential (1–5), the firms became more likely to adopt e-commerce.

Similarly, as the attitude of the owner of the enterprise moves from not important to essential (1–5), the firms were more likely to adopt e-commerce. This implies that only a positive attitude of the owner towards technological adoption and the e-readiness of customers and suppliers were the actual non- technological determinants towards technological adoption among SSEs. According to this study, the size of the customer does not really matter in determining if a firm will adopt e-commerce or not because a firm may decide to make all business transactions electronically even if the customer size is not too large.

Previous studies such as Ibrahim (2008), Ochola (2013) and Kabanda (2013) and have shown that infra- structural support and government policies, amongst others, are strong determinants of e-commerce adoption. The findings of this study contradict them as it proposes that even when all other non-technological factors are in place, but customers are not ready to adopt a technology, then nothing will happen. In like manner with the attitude of the owner of the enterprise: if the owner of the business has not thought of e-commerce as a wonderful idea, he or she will not embrace it in his or her firm. It is sufficient to say, all things being equal, that the level that SSEs are in terms of technological adoption is solely dependent on the attitude towards technology of the individual owners of the enterprises and supplier and customer readiness to adopt that technology. This corroborates Yu, Lu and Dong (2010) who established that top management com- mitment is extremely important at both the initial adoption and the institutionalization stage of e-commerce. It is also

Table 6: Correlation between non-technological factors and e- commerce adoption.

Non-technological factors R Sig Readiness of customer and supplier to conduct business electronically

0.612 0.000

Size of customer −0.582 0.000 Industrial support −0.579 0.000 Competition in the industry −0.566 0.000 Infrastructural support −0.555 0.000 Government policies 0.684 0.000 Attitude of the owner 0.598 0.000

Table 5: Impact of technological factors on e-commerce adoption.

B S.E t Sig Exp (B) Number of employee with ICT background 3.507 0.940 3.730 0.000* 33.348 Number of computer devices in your enterprise −1.044 1.407 −0.742 0.458 0.352 Training 1.586 2.073 0.765 0.444 4.886 Frequency of training −1.400 0.801 −1.748 0.302 0.091 Existing technologies −0.790 1.057 −0.747 0.455 0.454 Internet bandwidth 4.874 0.725 6.722 0.000* 130.831 Quality of Internet service 2.343 0.861 2.721 0.000* 10.412 Internet security technologies 3.174 0.582 5.454 0.000* 23.903

*At a 5% level of significance. p < 0.05; R2 = 0.805.

478 Ajao, Oyebisi, and Aderemi

in support of Zhai (2011) whose report showed that if business owners perceive that market forces are ready for e-commerce, they are likely to adopt e-commerce.

The model’s chi square (χ2) has 7 degrees of freedom, a value of 182.294 and a probability of p < 0.05. Thus, the indication is that the model fits well. The model summary shows that Nagelkerke’s R2 is 0.541, indicating a moder- ately strong relationship of 54.1% between the predictors and the prediction. This can be translated to mean that the model only account for 54.1% of the variance in the vari- ables. The findings of this study corroborate the theory of planned behaviour by Fishbein and Ajzen (1975) which states that the likelihood of adopting a technology will be strong if the individual who owns the enterprise hold a favourable attitude towards the adoption of that technology.

The impact analysis of the non-technological factors influencing e-commerce adoption in the SSEs gave a different result when the SSEs where investigated accord- ing to their sectors.

The result in Table 6 shows that in the furniture indus- try, besides the readiness of customer and supplier to conduct business electronically (exp B = 6.613; p < 0.05) and the attitude of the owner of the enterprise towards e- commerce (exp B = 5.008; p < 0.05), the size of the custo- mer (exp B = 2.907; p < 0.05) of the enterprise also plays a significant role in influencing whether a firm will adopt e- commerce or not. The implication of this is that the larger

the customer base of a firm, the higher the inclination towards adopting e-commerce once there is a high level of e-readiness from the supplier and customer and a posi- tive attitude from the owner of the enterprise. The furniture industry in Nigeria has a lot to do with thorough manual labour because of the processes the wood must pass through before fine furniture can be produced. Taking the time required for the rigorous work into consideration, a firm would not want to use both the time and the human resources that could be invested in the production process for the implementation of e-commerce. A furniture enter- prise would only do this if the owner perceives e-com- merce as a beneficial business tool coupled with a high demand for e-commerce from customers (Table 8).

Similarly, in the leather industry, the significant non- technological factors influencing the adoption of e-com- merce was the customer and supplier e-readiness (exp B = 3.126; p < 0.05), competition in the industry (exp B = 2.784; p < 0.05) and the attitude of the owner (exp B = 9.796; p < 0.05) as shown in Table 9. The leather industry is a proactive industry because it has a wide variety of pro- ducts. The microenterprises in the leather category adopted e-commerce more often than the remaining two enterprises. The justification for this could be as a result of the high level of competition in the industry. Also, leather products come in as handy eradicating the bottle- neck faced by the furniture category.

Table 7: Impact of non-technological factors on e-commerce adoption.

B S.E. t Sig. Exp (B) Readiness of customer and supplier to conduct business electronically 2.484 0.789 3.148 0.000* 11.989 Size of customer 0.090 0.223 0.403 0.688 0.914 Industrial support −0.404 0.321 −1.258 0.177 1.498 Competition in the industry −0.026 0.153 −0.169 0.433 0.975 Infrastructural support −0.299 0.211 −1.417 0.156 0.741 Government policies 0.510 0.249 2.048 0.000* 3.601 Attitude of the owner 1.018 0.250 4.072 0.000* 2.768

*At a 5% level of significance. p < 0.05; R2 = 0.541.

Table 8: Impact of non-technological factors on e-commerce adoption in the furniture SSEs.

B S.E. t Sig. Exp (B) Readiness of customer and supplier to conduct business electronically 1.889 0.700 2.696 0.000* 6.613 Size of customer 1.067 0.149 7.161 0.000* 2.907 Industrial support 0.069 0.686 0.100 0.920 1.071 Competition in the industry 0.377 0.374 1.008 0.313 1.458 Infrastructural support −0.859 0.589 1.458 0.145 0.424 Government policies −0.316 0.530 −0.596 0.495 0.697 Attitude of the owner 1.611 0.726 2.219 0.000* 5.008

*At a 5% level of significance. p < 0.05; R2 = 0.688.

Table 9: Impact of non-technological factors on e-commerce adoption in the leather SSEs.

B S.E. t Sig. Exp(B) Readiness of customer and supplier to conduct business electronically 1.140 0.721 0.194 0.008 3.126 Size of customer 0.095 0.348 0.272 0.785 1.099 Industrial support 0.422 0.657 0.642 0.521 1.525 Competition in the industry 1.024 0.273 3.751 0.000* 2.784 Infrastructural support 0.446 0.467 0.955 0.340 1.561 Government policies −0.417 0.375 −1.112 0.265 0.659 Attitude of the owner 2.282 0.567 4.024 0.000* 9.796

*At a 5% level of significance. p < 0.05; R2 = 0.545.

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This study reveals that the presence of competition increases the quest for technological adoption because it is believed that microenterprises that go with the moving trend in technology are for the elites and would have high quality products. This supposition places such firms in the realm of attention in any markets, opens up the markets for them which, in turn, increases sales. This explanation justifies why supplier and customer e-readi- ness, attitude of the owner of the enterprise and compe- tition influenced the adoption of e-commerce in the leather category and also supports why this industry had more adopters. The result of the findings from the leather category supports Kshetri (2008) who suggested that the competition among firms in an industry can influ- ence the implementation of a technological system.

Finally, the clothing and textile industry had customer and supplier e-readiness (exp B = 4.145; p < 0.05), size of customer (exp B = 1.411; p < 0.05), government policies (exp B = 2.918; p < 0.05) and attitude of the owner of the enterprise (exp B = 5.109; p < 0.05) as the significant factors influencing e-commerce adoption as shown in Table 10.

Local content policy and the prevalence of a weak naira, which made the importation of wares too expensive, propelled entrepreneurs to look inward at how to refurbish locally made product to measure up to what had been imported. Once an entrepreneur considers that his or her finished work is good enough to be exhibited, he or she moves further by incorporating technology into the business. In the clothing category this paper showed that the adoption of e-commerce would help promote the patronage of locally made product, hence the basis for adopting e-commerce. This result is in agreement with Iddris (2012) who said that government creating a suppor- tive environment for e-commerce adoption that focuses on policies would encourage e-commerce adoption.

The essence of the sector analysis was to inform speci- ficity for better policy implementation, so as not to general- ize that the same factors affect them all. This paper places emphasis on the fact that though small enterprises are all categorized to be the same, they still have their peculiarities when it comes to the factors influencing e-commerce adop- tion; hence, policy implementation in this area should also be in different directions. What is the most pressing for small enterprises in the clothing sector could be the least pressing for those in the furniture sector.

Conclusions This study assessed the level of e-commerce in selected SSEs in Southwestern Nigeria. It revealed that the firms

adopted e-commerce at the lowest operational level of e- commerce. The rate of adoption does not coincide with the level of adoption. The study also revealed that despite the fact that majority of the firms examined had adopted e-commerce, only a few had achieved implemen- tation at the institutionalization stage and, thus, there is need to accelerate implementation. The study also estab- lished the technological and non-technological factors that either hampered or fostered e-commerce adoption among the firms. Technological factors had more impact on e-commerce adoption compared with non- technologi- cal factors, although this cannot be considered out of the ordinary because the application of e-commerce is tech- nology based.

Strategic implications for policymaking and practice To enhance effective adoption of e-commerce technol- ogies among small enterprises and to ensure that its adop- tion translates into improvement of firm performance for sustainable development, it is important that:

(i) government should create an enabling business environment characterized by good infrastructural facilities like stable electricity power supply and good Internet network services especially in areas that are clustered with business activities;

(ii) government should drive implementation strategy that monitors the availability and quality of Internet service provided by network operators to SSEs, given that poor-quality Internet hampers e-commerce adoption. Internet should not only be available and cheap, it should be of good quality too that can be used regularly without unnecessary disruption;

(iii) government should create programmes that increase the acceptability of e-commerce among artisans so that they can be informed on why e-commerce should be harnessed to its utmost level in their business;

(iv) the general public should give preference to state of the art technologies. The study found out that attitude of the owner of firms towards e- commerce technologies and e-readiness of customers and suppliers had a significant influence on its adoption;

(v) SSEs should improve their intellectual capital by making necessary investment in human capital devel- opment characterized by training of staff in the area of technological advancement;

(vi) SSEs employ at least one staff member with an edu- cational background in technology management or a

Table 10: Impact of non-technological factors on e-commerce adoption in the clothing SSEs.

B S.E. t Sig. Exp(B) Readiness of customer and supplier to conduct business electronically 1.422 0.660 2.154 0.000* 4.145 Size of customer 0.344 0.503 0.683 0.003 1.411 Industrial support −0.470 0.604 −0.778 0.437 0.625 Competition in the industry 0.575 0.269 2.137 0.003* 0.563 Infrastructural support −0.630 0.503 1.254 0.168 0.500 Government policies 1.071 0.437 2.451 0.002* 2.918 Attitude of the enterprise 1.630 0.543 3.000 0.000* 5.109

*At a 5% level of significance. p < 0.05; R2 = 0.621.

480 Ajao, Oyebisi, and Aderemi

computer-related area so as to be able to answer the global call of digitization.

Disclosure statement No potential conflict of interest was reported by the authors.

ORCID H. O. Aderemi http://orcid.org/0000-0003-1706-4716

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