INTRODUCTION - Oakland University
eCommerce Adoption in Developing Countries: A Model and Instrument
Alemayehu Molla(
IDPM
The University of Manchester
Oxford Road, Manchester M139GH, UK
Tel: +44-161-275 3233
Fax: +44-161-273 8829
Alemayehu.Molla@man.ac.uk
Paul S. Licker
Department of Decision and Information Sciences
Oakland University
Rochester, Michigan 48309 USA
Tel: 1-248-370-2432
Fax: 1-248-370-4604
licker@oakland.edu
Abstract
This paper uses the extant literature relating to the adoption of information technology and eCommerce supplemented by interviews with experts working in the field to propose and operationalize a model of eCommerce adoption particularly relevant to developing countries. The model focuses on the concept of readiness for ecommerce. Existing models of eCommerce readiness focus either on the innovation itself, factors internal to companies or on the external (generally country) environment within which the technology works for the company. The model proposed here brings together all views using an interactionist perspective. We posit that the adoption of eCommerce innovation in developing countries is affected by the dynamic interaction among the organization and its environment. A three-factor perceptual model (perceived organizational eReadiness, perceived external eReadiness and stages of adoption) is explicated, a research instrument is determined, purified and validated, and a set of research-ready scales is proposed for use in assessing eCommerce adoption in developing countries.
Keywords : eCommerce, eCommerce Adoption, Developing Countries, Perceived eReadiness
1. INTRODUCTION
Because of the network externalities feature of eCommerce, its adoption by organizations of developing countries is purported to have an impact far beyond their borders in affecting global productivity [66, 45]. Arguably, the salient obstacles to the adoption of eCommerce in many of the developing countries could relate to the lack of necessary infra and infostructure. Several of the studies in the area are therefore concentrated on identifying the macro-level infrastructure, institutional and social constraints organizations in developing countries face to harness eCommerce’s potential [1, 12, 16,24, 30,41, 60, 63]. So far, only a few studies have studied organizational issues that either facilitate or inhibit eCommerce adoption in developing countries [2, 9, 49]. Perceived potential benefits and existing technological capacities, organizational size and complexity and presence of electronic business culture are some of the variables identified in previous studies. While each of these studies has individually contributed to the understanding of eCommerce in developing countries, we would argue that there still remain theoretical and practical reasons to examine an eCommerce adoption model in developing countries.
With regard to theory, most of the studies have been approached in a pragmatic manner with little recourse to conceptual models let alone theories. Indeed one of the shortcomings of the information technology in developing countries literature is its failure to either articulate or align itself with coherent theories that are grounded on sound ontological and epistemological principles [39]. From a practical point of view, the studies address a limited and possibly arbitrary repertoire of variables restricting their scope of investigation to some environmental or organizational issues while neglecting others. In addition, there is a lack of consensus in defining the dependent variable, that is the domain of eCommerce being adopted, and such inconsistency limits the development of a cumulative body of knowledge.
Overall, a close examination of the literature reveals that there is a dearth of systematic information and thought on what drives eCommerce adoption in developing countries. In particular, there doesn’t appear to be an appropriate model that can be applied in the investigation of the determinants of eCommerce adoption by business organizations of developing countries. The goal of this study is therefore to provide a meaningful contribution to an area of research sorely lacking. The purpose is three fold: first it attempts to fill the void in eCommerce research in developing countries, second it proposes a model of eCommerce adoption that is robust enough to cover the idiosyncratic nature of the businesses and the contexts of developing countries and third it develops an instrument to operationalize the proposed model.
2. THEORETICAL BACKGROUND
Previous research suggests innovation diffusion as one of the theoretical frameworks to study eCommerce [70]. The literature on the adoption of an innovation promotes four dominant perspectives: organizational imperative [26, 40]; technology imperative [56, 57], environmental imperative [38, 43] and interactionism [27, 46, 47].
Organizational and technological imperative models consider the complexity, compatibility, relative advantage and other attributes of the innovation and the strategic choice, risk taking propensity and innovativeness of adopting organizations and their leaders as major determinants of adoption. Because of the conditions under which organizations in developing countries operate, these two imperatives alone do not sufficiently address what might affect the adoption of e-commerce in general and in developing countries in particular. There don’t seem to be any cultural, individual or economic imperatives in these approaches, either. For instance, organizational imperative models assume certainty as to the availability of choices from the environment and argue for positive relationships between, for example, innovative managers’, their IT background and technology adoption. Nevertheless, managers in developing countries, despite having the ‘innovative’ attributes, might not have a range of options from which to choose an innovation and the environment might put certain constraints on the kinds of innovations they aspire to [37, 42, 68]. It then follows that the adoption of eCommerce cannot be adequately understood without an analysis of the external environment.
Environmental imperative models, on the other hand, tend to respond to contextual influences only and dominate most studies of eCommerce in developing countries. For instance, some [30,1] argue that eCommerce is unthinkable without legal system, telecommunications regulations and financial infrastructure. Transportation, delivery, telecom infrastructure, software industry, and e-payment have also been identified as determinants of eCommerce in developing countries [63]. Other studies also promote this same notion of environmental imperative as affecting eCommerce and other ICT innovations adoption in developing countries [16, 23, 41]. In particular, environmental imperative thought is the underlying framework of the literature on e-commerce readiness, which is a conventionally accepted way of explaining e-commerce diffusion in developing countries.
Over the past two to three years a number of e-readiness assessment tools have been developed and many countries have been assessed for their e-readiness [7]. While the result from such line of enquiry succeeds in highlighting the manifold challenges that businesses in developing countries have to overcome and in identifying the weaknesses of the developing countries’ context, it does not sufficiently explain eCommerce uptake variations among organizations operating in the same context. In addition, it does not help to address what drives organizations in developing countries that incorporate eCommerce into their operation while operating within the conditions that have been identified as “eCommerce unfriendly” nor does it enable us to discriminate adopters from non-adopters or the degrees of adoption, that is modes of use.
Generally, the three imperatives focus either on the innovation or the organization or the environment only. The fourth imperative – interactionism- allows the treatment of all the three forces and their interaction in one dynamic framework. The perspective assumes a co-influence between the forces of the external environment and the internal organization such that the external environment determines the internal organization, which in turn through deliberate (articulating a problem or formulating a solution) and at times unintentional actions shapes the conditions external to the organization [4, 22, 47]. The interaction is cyclical in that the altered environment in turn produces new conditions for managerial actions either to reinforce or change the external environment making the model dynamic. The interactionism model, with its spark of mutual co-influence between the environment and the organization, can explain marked differences in the performance of organizations in identical contextual situations [39]. In addition, it also allows deciphering why certain kinds of innovations are successful in a given organization while other kinds of innovations are not so successful.
In this study, we follow the interactionism imperative as a theoretical root. Working from an interactionism perspective, it can be posited that the adoption of eCommerce in developing countries is affected by the dynamic interaction between the organization and its environment. While the external environment can present a blend of eCommerce constraints and niches, the internal organization and management capacities of businesses in developing countries to handle specific demands of eCommerce would also determine how an organization would be able to respond to such forces [35, 45]. Therefore, an audit of both the internal organizational and external contextual determinants of eCommerce can provide meaningful predictors of eCommerce adoption in developing countries.
Managers’ synchronic assessment of both their external and internal contexts for eCommerce gives a concept that we call perceived eReadiness [34]. We define “perceived eReadiness” as organizations’ assessment of both the organizational and external situations in making decisions about adopting e-commerce. It indicates the status of the organization vis-à-vis the demands and requirements of eCommerce. We hypothesize that perceived eReadiness explains e-commerce adoption variation among organizations in developing countries and refer to our model as the Perceived eReadiness Model (in short, The PERM)[1]. The concept of the PERM has two constructs- Perceived Organizational eReadiness (POER) and Perceived External eReadiness (PEER). Taken together, PEER and POER are hypothesized to predict e-commerce adoption and explain a significant part of the variance in the level of e-commerce adoption in developing countries. Figure 1 captures the general structure of the model.
Figure 1 The PERM General Structure
3. RESEARCH METHODS
Before designing the instrument to be used to operationalize the PERM, existing instruments were searched. While there are a number of instruments for assessing eReadiness [7,20], none of them have been found to be appropriate for the purposes of this research. This is because most of them are designed to assess the eReadiness of a given economy or community or region. However, the primary unit of analysis in this research is the organization. On the other hand, instruments that assess organizational level eReadiness (such as those found in [20]) could not be used for our research because they have inherent assumptions about the level and sophistication of eCommerce activities on a par with Cisco, Oracle and Amazon. These assumptions do not necessarily hold in developing countries. Overall, in order to ensure accuracy and validity of the instrument and to reduce the measurement error, the instrument development procedure suggested by Churchill [8: 66] was followed. The procedure involves specifying the domain of constructs, generating representative sample of items, purifying the measure through a pilot study, and collecting further data and assessing the validity and reliability of the measure.
3.1. Specify Domain of Construct
Defining a construct’s theoretical meaning and conceptual domain are necessary steps in developing an accurate and a content valid instrument [8]. Two approaches were used as an interpretive lens in identifying the theoretical constructs of the PERM. These are the socio-technical systems (STS) and competitive context approaches. Socio-technical systems (STS) is an extensive body of conceptual framework underlying the introduction of innovations into organizations. The approach is based on the premise that organizational performance hinges on how well the social and technical systems of the organization are designed and collectively tuned to provide a capacity to proactively interact with the environment [10, 65]. STS is also a useful framework to understand why organizations’ results are what they are and how the integration of the social and technical systems leads to improved results [59].
The STS has particular relevance to understanding organizations in developing countries where resources and social issues have been identified as some of the chief challenges in the implementation of IT [21, 23, 36, 55]. In short, STS implies that one can study organizations from the points of view of the organizational process that must be included; organizational structure that must be in place; the (information) technology and other resources that must be available; and the social and cultural assumptions in the organization [28, 65]. The relevance of the above to the proposed model of e-commerce adoption is clear. It helps to systematically define the constituents of an organization. The emphasis is thus to assess an organization’s eReadiness in terms of its main components as perceived by its top managers.
The other approach is the competitive context analysis [52] and focuses on national circumstances rather than the narrower range of organizational performance. Competitive context analysis provides a comprehensive and empirically supported framework for analyzing the role and importance of national circumstances that define the environment within which firms survive and thrive. In this approach, demand conditions, related and supporting industries and government are some of the most important attributes that shape the environment in which local firms operate and compete [52]. Further, firms must understand the home nation contexts that are most crucial in determining their ability to operate successfully. This framework is particularly relevant in investigating perceived external eReadiness (PEER) and identifying the salient environmental factors that might affect eCommerce adoption.
The above two approaches provide the basic language and analytical framework for a fair investigation of the organizational and environmental variables that might affect eCommerce adoption in developing countries. In addition to the above procedure, exploratory interviews and informal discussions were conducted with three academicians and three consultants who have relevant experience in eCommerce issues in developing countries. The main purpose of the discussions was to get the views of the experts on the main constructs of the study and to have initial ideas on the items to be included in the instrument. The discussions provided helpful insights in refining the definitions of the constructs’ domain.
On the basis of these premises and with due reflection to the challenges and realities of developing countries, a finer-grained definition of the major constructs were obtained. Perceived organizational eReadiness (POER) is thus defined as managers’ evaluation of the degree to which they believe that their organization has the awareness (A), resources (R) commitment (C) and governance (G) to adopt e-commerce. Perceived external eReadiness (PEER), on the other hand is the degree to which mangers believe that the market forces, the government and other supporting industries are ready to facilitate their organizations’ e-commerce implementation.
The dependent variable in the model is eCommerce adoption. Because eCommerce adoption can take various forms and complexities, for operational reasons and in order to make the proposed model tractable, it was instructive to differentiate between entry-level adoption and the extent of such adoption. Whereas we refer to the first as initial eCommerce adoption, we call the second as the institutionalization of eCommerce. This is consistent with the model of technology adoption proposed in previous research [72] and enables to cover not only the factors that affect initial adoption but also those that might affect the maturity of e-commerce within organizations once the initial decision is taken. To operationalize the two dimensions of eCommerce adoption, we have used the eCommerce maturity model. Despite differences in the number and naming of the stages in each model, eCommerce researchers appear to accept that organizations follow certain migration paths in moving to eCommerce [13, 33]. From the variations of phases in the literature, a six-phase eCommerce status indicator closely related to the current eCommerce realities of developing countries can be identified. The phases are no eCommerce, connected eCommerce, static eCommerce, interactive eCommerce, transactive eCommerce and integrated eCommerce.
Many researchers [25, 30, 64] accept interactive eCommerce (a status which allows conducting at least two of the transaction lifecycle stages electronically: searching and ordering) as the beginning of eCommerce. Therefore, the first measure, initial eCommerce adoption, is operationalised as a dichotomy of whether or not a business has achieved this status. Following this, a business is to be defined to have adopted eCommerce if it has attained an interactive eCommerce status. The second measure of eCommerce adoption, institutionalization of eCommerce, indicates the extent of eCommerce utilization. This measure is opertionalized by looking into whether or not an organization has attained an interactive or transactive or integrated eCommerce status. Figure 2 captures the conceptual model and table 1 summarizes the definitions of the variables in the model.
Figure 2 Conceptual Representation of the PERM
Table 1 Description of the Variables in the PERM
|Variables |Description |References |
|Awareness |Represents perception of eCommerce elements in the environment; comprehension of |[2, 9, 15, 19, 31, 34, |
| |their meaning through an understanding of eCommerce technologies, business models,|66, 67] |
| |requirements, benefits and threats and projection of the future trends of | |
| |eCommerce and its impact. | |
|Commitment |Reflects enough energy and support for eCommerce from all corners of an |[2, 9, 20, 21, 22, |
| |organization and especially from the strategic apex. It refers to having a |37,54, 68] |
| |clear-cut eCommerce vision and strategy championed by top management, eCommerce | |
| |leadership and organization wide buying-in of eCommerce ideas and projects. | |
|Human Resources |Refer to the availability (accessibility) of employees with adequate experience |[21, 20, 22, 53, 73,74]|
| |with and exposure to computing and Internet technologies and other skills (such as| |
| |marketing, business strategy) that are needed to adequately staff eCommerce | |
| |initiatives and projects. | |
|Technological Resources |Technological resources refer to the current information and communication |[21, 20, 22, 53, 73,74]|
| |technologies base of an organization. It assesses the extent of computerization, | |
| |the flexibility of existing systems and experiences with network based | |
| |applications | |
|Business Resources |This covers a wide range of capabilities and most of the intangible assets of the |[21, 20, 22, 53, 73,74]|
| |organization. It includes the openness of organizational communication; risk | |
| |taking behaviour, existing business relationships, and funding to finance | |
| |eCommerce projects. | |
|Governance |The strategic, tactical and operational model organizations in developing |[20, 34, 45, 71] |
| |countries put in place to govern their business activities and eCommerce | |
| |initiatives. | |
|Government eReadiness |Organizations’ assessment of the preparation of the nation state and its various |[7, 32, 38, 43, 48, 52,|
| |institutions to promote, support, facilitate and regulate eCommerce and its |67] |
| |various requirements. | |
|Market forces eReadiness |The assessment that an organization’s business partners such as customers and |[3, 24, 34, 52, 61] |
| |suppliers allow an electronic conduct of business. | |
|Supporting Industries |Refers to the assessment of the presence, development, service level and cost |[34, 49, 52, 63, 67 |
|eReadiness |structure of support giving industries whose activities might affect the eCommerce| |
| |initiatives of businesses in developing countries. | |
|Initial eCommerce adoption |A business is considered to have adopted eCommerce if it has achieved an |[34] |
| |interactive eCommerce status. | |
|Institutionalization of |Indicates whether or not an organization has attained an interactive, or |[34] |
|eCommerce |transactive or integrated eCommerce status. | |
3.2. Initial Instrument Preparation
The initial instrument was prepared in two stages. First, through an extensive search of the existing literature and using the insight obtained from the exploratory consultations and interviews with the experts, an initial pool of 136 items was generated [44]. The items were further reviewed and edited to capture the essence of the concepts and constructs of the study and the preliminary instrument containing 88 items was produced. Following [11], a panel of 20 experts including the six involved in the discussion of the domain of construct was contacted to review and pretest the instrument. The panelists were selected on the basis of their experience (either practical or research) and knowledge of eCommerce issues in developing countries and represent both academicians and practitioners.
The experts were asked to judge the degree of relevance of each of the items in the preliminary instrument as possible measures of the individual research variables on a five-point Likert-type scale ranging from extremely relevant (5) to not at all relevant (1). They were also encouraged to suggest additional items they believe are not covered in the instrument. Responses were obtained from 16 members of the panel. To check how well two or more evaluators agree in their assessment of a variable, the inter-observer reliability was evaluated using correlation coefficients [29]. At p( 0.01, all of the inter-rater (correlation coefficient between different judges) and corrected rater-total (correlation coefficient of the individual rater to the total score, excluding the rater’s score) correlations were significantly high supporting the stability and reliability of the experts’ judgment (for the result, see Appendix 1).
To discern the relevant items based on the experts’ judgment, the mean relevance score (MRS) was computed for each of the items in the preliminary instrument. A total of 17 items whose MRS was less than average, that is 2.5, were excluded from the instrument. The panel of experts also suggested additional items for some of the variables and modifications in the wordings of some of the items. After careful examination of the suggestions by the researchers and discussion with other experts, three of the additional items suggested were introduced into the instrument and the statements were further edited to make their wordings as precise as possible. Overall, the procedures adopted to define the domain of the construct, pool a significantly large number of items and test their relevancy using a panel of experts in the field can be considered as adequate to satisfy the test for content validity. The 74-item instrument (Appendix 2) was then ready to be piloted.
3.3. The Pilot Study
The purposes of the pilot study were to establish the basic unassailability of the model before scale purification and to eliminate duplicative items, that is, items sharing the same underlying concept [50]. Meanwhile, the pilot study was also intended to check for questions and instruction clarity and in particular to check if businesses could identify the clarity of the eCommerce status indicators used to operationalize eCommerce adoption. The instrument used in the pilot study was developed using a five-point Likert-type scale ranging from strongly agree (1) to strongly disagree (5). The instrument was pilot tested in a survey of 60 randomly selected business organizations in South Africa. After three weeks and follow up efforts, a total of 12 responses were obtained. The response was adequate for the purposes of the pilot study [50, 69]. In addition, telephone discussions were held with four of the respondents to establish instruction clarity and difficulties experienced in completing the questionnaire.
To test the soundness of the model, correlation coefficients were examined for all pairs of the items within the two research constructs, that is, POER and PEER. In each pair where the correlation coefficient was significant at p=0.001, one item within the pair was considered for elimination to enhance instrument readability and parsimony [50]. Before deleting any item, the impact of such deletion on the domain coverage (that is content validity of the construct) was evaluated to ensure that the domain coverage would not suffer as the result of the deletion. In addition, the measure’s corrected-item-total correlation was investigated to assess the improvement on the reliability of the measure as a result of dropping a particular item. Where an item was deleted, the item in the pair with the lowest corrected item-to-total correlation was dropped [50, 69]. After this exercise, four items (A6, C6, R18, GVeR1 in Appendix 2) were dropped from the instrument leaving a total number of 70 items with an initial reliability of 0.91 and 0.70 for POER and PEER respectively. At the end of the pilot study, we believed that the instrument has adequate face validity; it has a preliminary reliability within an acceptable range and passes the test for parsimony and readability. In addition, the questions and instructions were reported to be clear.
3.4. The Full Study
The data were collected using a questionnaire administered in South Africa. A covering letter explaining the purposes of the research; assuring anonymity of both respondents and their organization and providing instructions on how and who to complete the questionnaire together with the questionnaire and a postage-paid, self-addressed return envelope was sent out to the managing directors of each of the organizations. The mailing went to 1000 organizations selected using a systematic sampling technique from a reputable business directory that has been in publication in South Africa for over 60 years. Follow-up efforts were made through phone calls and email. In addition, a second wave of the instrument was administered to a random sample of non-respondents [see 5, 17]. The two rounds of survey were conducted over a period of 120 days. 125 questionnaires bounced back as undeliverable because either the businesses had closed or changed their address. Out of 169 total responses, 19 were found to be incomplete resulting in 150 usable responses, that is, a 19% response rate from the 875 deliverable questionnaires. This sample size is considered as adequate for the analysis and is comparable to response rates reported in the IS literature [51].
4. ANALYSIS AND RESULTS
An analysis was conducted in order to test the instrument’s validity and reliability. Based on the instrument validation literature [6, 8, 44, 62], the analyses proceeded in several stages. First the initial reliability of the instrument was assessed in order to purify it from the “garbage items”, that is, items which do not have the common core, but which do produce additional dimensions in a factor analysis [see 8,11]. Second to assess whether or not the measures chosen are true constructs that describe an event, the construct validity of each items was examined. Convergent and discriminant validity were tested to assess if the measure is similar within itself while sufficiently different from other measures. Finally, the predictive validity and final reliability of the instrument were assessed.
4.1. Initial Reliability
To test the initial reliability of the model, the coefficient alpha and item-scale correlation were calculated [8,11]. The corrected item-scale correlations were plotted in descending order and items with item-scale correlation below 0.4 or whose correlations produced a substantial or sudden drop in the plotted pattern and which would raise the alpha if deleted were eliminated (Appendix 3). The cutoff is judgmental and Churchill’s [8] suggestion is to eliminate items with item-scale correlation “near zero”. However, the cutoff used is comparable to cutoffs used by researchers in similar exercises [50, 69]. As the result of the item analysis process, four items from POER (A1, A8, R3, and R13) and one from PEER (GVER6) were dropped from the instrument. All the remaining correlations with the corrected item-scale (r ≥ 0.4) were significant at p= 0.05. Thus, the cutoff values were considered high enough to ensure that the items retained were adequate measures of the constructs. In addition, the Cronbach alpha values (0.93 for POER and 0.79 for PEER) satisfy the highest minimum criterion (0.8, approximated to one digit) of reliability [44] and provide evidence of initial reliability to proceed with further validity checks.
4.2. Construct Validity
Principal component analysis was used to test the construct validity of the instrument. In order to extract the factors, the following factor extraction rules [18,62] were implemented:
1. Casewise deletion of missing data.
2. Using a minimum eigenvalue of 1 as a cutoff value.
3. Dropping items with a factor loading less than 0.5 on all factors from subsequent iterations.
4. Dropping items with a factor loading greater than 0.5 on two or more factors from subsequent iterations.
5. Exclusion of single item factors for the sake of parsimony.
6. Component wise with Varimax raw rotation factor extraction.
Using the iterative sequence of factor analysis, nine items (R4, R9, R10, R15, R21, C7, G4, G5, G9 in Appendix 2) from the POER construct were eliminated. After eliminating the items using the above set of criteria, the factor analysis resulted in a final instrument of 33 items representing 6 distinct variables for POER and 10 items in three factors for PEER. The factor analysis for the organizational and external eReadiness dimensions further indicates that except for one variable (C8-our employees at all levels support our eCommerce initiatives), which was expected to load to the commitment variable but rather loaded on the governance variable, the rest of the items uniquely load into their hypothesized variables. Accordingly, for the subsequent analysis, C8 was included within the governance construct. Table 2 presents the final factor loadings.
4.3. Convergent and Discriminant Validity
Convergent and Discriminant validity are components of construct validity and refer to whether the measure is similar within itself and yet sufficiently different from other measures. In general, the significant loading of the items on single factors indicates the unidimensionality of each construct, while the fact that cross-loading items were eliminated supports the discriminant validity of the instrument. However to evaluate the convergent and discriminant validity of the instrument further, the correlation matrix approach [14, 50, 69] was applied.
Evidence about the convergent validity of a measure is provided in the validity diagonal (items of the same factor) by observing the extent to which the correlations are significantly different from zero and sufficiently large enough to encourage further test of discriminant validity (Appendix 4). The smallest within-factor (intra-factor) correlation for each factor was awareness, 0.32; human resources 0.77; Business resources 0.30; technology resources 0.45; Commitment 0.48; Governance, 0.37; Market forces eReadiness 0.63; Government eReadiness 0.28; and Supporting industries eReadiness 0.30. These correlations are significantly higher than zero and large enough to proceed with discriminant validity analysis.
Discriminant validity tests the degree to which a variable differs from other variables. To claim discriminant validity, an item should correlate more strongly with other items of the same variable than with items of other variables. For each of the items, discriminant validity was tested by counting the number of times (K) that the item correlates higher with items of other factors than with items of its own factor. For example, the lowest item-factor correlation for A4 is 0.47 and this correlation is higher than A4’s 26 correlations with items of all other variables within the POER dimension, that is, the value of K equals zero. To provide evidence of the discriminant validity of a measure, the value of K should be less than one-half of the potential comparisons [50, 69]. Table 3 summarizes the values of K from all the comparisons.
Table 2. Summary of Factor Analysis of the PERM Variables
| |
Table 3 Summary of K-count to Test Discriminant Validity
|Category |No of |No. of Comparisons |Maximum Acceptable|Instances of |
| |items |for each item in the|K |Validity |
| | |scale | |Violation |
| | | | | |
|Awareness |7 |26 |13 |- |
|Human Resources |2 |31 |15 |- |
|Business Resources |6 |27 |13 |- |
|Technology Resources |5 |28 |14 |- |
|Commitment |5 |28 |14 |- |
|Governance |8 |25 |12 |- |
|Government eReadiness |4 |6 |6 |- |
|Market Forces eReadiness |2 |8 |4 |- |
|Supporting Industries eReadiness |4 |6 |6 |- |
|Total |43 | | |- |
An examination of both Table 3 and the correlation matrix from which the table was extracted (Appendix 4) reveals no violations of the discriminant validity in a total of 950 comparisons. In fact, K is zero for 17 of the items; less than 3 for 81% of the items and approached the threshold point in only one of the cases (namely R9–availability of policy that encourages grass roots eCommerce initiatives). Thus, there is sufficient evidence of both convergent and divergent validity and therefore the instrument can be considered as adequate to generate quality data.
4.4. Predictive Validity
Predictive validity examines if the instrument distinguishes the different cases such as those with high-perceived eReadiness from those without. Correlations between the developed scales and the control variables were used to study the predictive power of each of the constructs [58]. Table 4 provides a summary of the correlation matrix.
Table 4 Predictive Validity Statistics Summary
| | | | | | | | |
|Research Variables |A11 |R5 |R16 |R24 |C9 |G11 |EER |
|Awareness |0.793 |0.447 |0.428 |0.328 |0.442 |0.295 |0.295 |
|Human Resources |0.382 |0.616 |0.342 |0.338 |0.383 |0.340 |0.155 |
|Business Resources |0.490 |0.491 |0.681 |0.471 |0.468 |0.295 |0.269 |
|Technology Resources |0.466 |0.500 |0.598 |0.801 |0.392 |0.412 |0.240 |
|Commitment |0.464 |0.533 |0.380 |0.433 |0.728 |0.609 |0.265 |
|Governance |0.498 |0.554 |0.413 |0.484 |0.641 |0.755 |0.264 |
|Government eReadiness |0.060 |0.246 |0.186 |0.286 |0.056 |0.191 |0.472 |
|Market Forces eReadiness |0.214 |0.384 |0.214 |0.348 |0.387 |0.229 |0.549 |
|Supporting Industries eReadiness |0.130 |0.252 |0.187 |0.256 |0.101 |-0.024 |0.538 |
| | | | | | | | |
All correlations between the major research constructs and their respective control variables in the organizational eReadiness and external eReadiness dimensions are quite high and significant at the 0.05 level, thereby showing evidence of predictive validity (Bold faces in Table 4).
4.5. Final Reliability
Table 5 shows the reliability of the final instrument and the alpha coefficients of the individual variables. Overall the final instrument has 6 items to operationalize eCommerce adoption; 33 items under POER and 10 items under PEER. To accept a measure as reliable cronbach alpha values of 0.80 for basic research and 0.90 for applied research appear to be widely accepted in the literature [18, 44]. As shown in Table 5, all the reliability coefficients satisfy the minimum criteria suggested in the literature. In addition, the research variables’ reliabilities are consistently close to their respective overall reliabilities of 0.93 and 0.79 and there is very little variation among the individual reliabilities within each of the two dimensions. This shows that the measure is sufficiently reliable and can consistently capture true score variability among respondents.
Table 5 Instrument Reliability
|Research Variable |No. of items |Cronbach alpha |
|Major construct | | |
|Awareness |7 |0.89 |
|Human Resources |2 |0.87 |
|Business Resources |6 |0.81 |
|Technology Resources |5 |0.85 |
|Commitment |5 |0.88 |
|Governance |8 |0.91 |
|POER |33 |0.93 |
|Market Forces eReadiness |2 |0.78 |
|Government eReadiness |4 |0.77 |
|Supporting Industries eReadiness |4 |0.75 |
|PEER |10 |0.79 |
| eCommerce Adoption |6 |--- |
5. THE PERM
Based on the tests and analyses described so far, the final PERM is provided in figure 3 and its instrument in appendix 5. Because of both the theoretical grounding and the stringent tests undertaken to examine the psychometric properties, the PERM represents a progress towards identification, measurement, operationalization and validation of organizational and environmental eReadiness variables that affect eCommerce adoption in developing countries. The model is unique in the sense that it departs from the conventional wisdom of looking into environmental characteristics only and looked into internal organizational capabilities and characteristics of businesses in developing countries.
Figure 3 The PERM for Assessing eCommerce Adoption in Developing Countries
Despite the steps undertaken to validate the model and ensure its reliability, we note some limitations that could be addressed in future research. First, additional items can be introduced to improve the coverage and reliability of the perceived external eReadiness measures. Second, while we have used principal component analysis, a confirmatory analysis and a multi-country and cross-cultural validation using other large samples gathered elsewhere are essential. This increases the greater validation and generalizability of this novel model and instrument. Subsequent studies would also allow assessing the test-retest reliability of the instrument.
6. CONCLUSIONS
If in fact eCommerce is going to emerge as a global conduit for conducting business, as most aficionados predict it, it is paramount that the practice of this phenomenon in developing countries be investigated and the factors that affect its diffusion properly understood. Current models of adoption are mostly characterized by either organizational, or technological, or environmental imperative views. In this study, we contend that the developing countries environment departs significantly from the Western business environment but it cannot be considered as the sole determining factor in affecting eCommerce adoption. Based on the interactionism perspective we proposed a model of eCommerce adoption in developing countries called the PERM; and identified, operationalized and tested its underlying constructs and variables. The study was conducted with due consideration to the realities of developing countries and thence offers a theoretically constructed and empirically validated model and instrument, although not necessarily a final solution, to investigate eCommerce adoption. We expect researchers that are interested in eCommerce adoption in developing countries to benefit significantly from this work. Testing the model proposed in this study and refining both its theoretical and operational constructs opens an exciting avenue of research and ensures the external validity of the PERM. We believe that the study provides the foundation for cumulative knowledge of eCommerce adoption in developing countries.
The instrument offers a framework for organizations in developing countries to analyze and manage potential risks that might threaten their migration to eCommerce. The broad coverage of both internal and external business areas with which organizations need to be concerned with provides a holistic approach to understanding eCommerce challenges. Using the instrument, businesses could reflect inwards to assess their internal organization and outwards to assess the external environment and identify gaps (and potential risks) that might hamper their eCommerce progress. Such exercises could lead to developing action plans to address the issues identified in the assessment phase. In addition, the tool could also be a valuable instrument in retrospectively identifying reasons for eCommerce failures thus facilitating higher-level organizational learning. We also expect other support giving institutions to benefit from the model and instrument suggested in this study. For instance, a periodical and longitudinal administration of the instrument, within a particular industry, to a representative cross-section of organizations and publishing the findings from such exercise could enable to assess the eCommerce context and address the needs of developing country organizations. Outputs from such studies will be crucial for policy makers in designing training and other support and intervention programs to encourage the adoption of eCommerce and the benefit that might follow from such adoption.
Appendix 2: Initial instrument used in the pilot study
Which one best describes your current eCommerce status
1. Not connected to the internet, no e-mail
2. Connected to the internet with e-mail but no web site
3. Static eCommerce, that is publishing basic company information on the web without any interactivity
4. Interactive eCommerce, that is accepting queries, e-mail; and form entry from users
5. Transactive eCommerce, that is online selling and purchasing of products and services including customer service
6. Integrated eCommerce, that is the web site is integrated with suppliers, customers and other back office systems allowing most of the business transactions to be conducted electronically
On the scale of 1 (Strongly Agree) to 5 (Strongly Disagree), indicate your level of agreement with the following statement
|Item ID |Description |
| |Our business considers that e-commerce is a North American trend not yet applicable to our environment |
| |E-commerce applications are becoming common with our partner organizations |
| |Businesses with whom our organization is competing are implementing e-commerce and e-business |
| |Our business recognizes the opportunities and threats enabled by e-commerce |
| |Our organization has a good understanding of e-commerce business models that are applicable to our business |
| |We have a good understanding of e-commerce application solutions that are applicable to our business |
| |We have a clear understanding of the potential benefits of e-commerce to our business |
| |Our organization believes that the gain from e-commerce outweighs its cost |
| |We consider that e-commerce has a tremendous impact on the way business is to be conducted in our industry |
| |We believe that businesses in our industry that are not adopting e-commerce and e-business will be at a competitive disadvantage|
| |In general our business has adequate awareness about e-commerce |
| |Most of our employees are computer literate |
| |Most of our employees have unrestricted access to computers |
| |Most of our employees have unrestricted Internet access |
| |We have created clearly defined, e-commerce career paths within our organization |
| |Our business has the necessary technical, managerial and other skills to implement e-commerce |
| |Our people are open and trusting with one another |
| |Communication is very open in our organization |
| |Our organization exhibits a culture of enterprise wide information sharing |
| |We have a policy that encourages grass roots e-commerce initiatives |
| |We are aggressive in experimenting with new technologies |
| |Failure can be tolerated in our organization |
| |Our organization is capable of dealing with rapid changes |
| |We have strong relationships with our suppliers and customers |
| |We have sufficient experience with network based applications |
| |We sufficiently invest in our e-commerce projects |
| |We have sufficient business resources to implement e-commerce |
| |Our organization is well computerized with LAN and WAN |
| |Our eCommerce solutions are interactive and allow two way communication |
| |Our existing systems allow us to make changes for e-commerce applications |
| |We have high bandwidth connectivity to the internet |
| |We have an established enterprise-wide IT infrastructure |
| |Our existing systems are flexible |
| |Our existing are customizable to our customers’ needs |
| |We have adequate technological capability for e-commerce implementations |
| |Our business has a clear vision on e-commerce |
| |Our vision of e-commerce activities is widely communicated and understood throughout our company |
| |Our e-commerce implementations are strategy-led |
| |All our e-commerce initiatives have champions |
| |Senior management champions our e-commerce initiatives and implementations |
| |We have staffed our e-commerce projects with the proper resources to achieve their goals |
| |We have an e-commerce mind-set throughout all levels of management |
| |Our employees at all levels support our e-commerce initiatives |
| |Our business demonstrates adequate level of commitment in e-commerce implementations |
| |Roles, responsibilities and accountability are clearly defined within each e-commerce initiative |
| |E-commerce accountability is extracted via on-going responsibility |
| |Decision-making authority has been clearly assigned for all e-commerce initiatives |
| |Our e-commerce managers are granted the authority to make decisions and take actions as opportunities arise |
| |Our managers demonstrate readiness for change |
| |We thoroughly analyze the possible changes to be caused in our organization, suppliers, partners, and customers as a result of |
| |each e-commerce implementation |
| |We follow a systematic process for managing change issues as a result of e-commerce implementations |
| |We define a business case for each e-commerce implementation or initiative |
| |There is smooth relationship between the business and internal IT organization |
| |We have clearly defined metrics for assessing the impact of our e-commerce initiatives |
| |We believe that we have an effective governance model in our e-commerce implementations |
|Item ID |Description |
| |We believe that our customers are ready to do business on the Internet |
| |We believe that our business partners are ready to conduct business on the Internet |
| |Our business considers Internet as a safe environment for conducting business |
| |We believe that there are effective laws to protect consumer privacy |
| |We believe that there are effective laws to combat cyber crime |
| |We believe that the legal environment is conducive to conduct business on the internet |
| |The government demonstrates strong commitment to promote e-commerce |
| |Government regulations allow electronic settlement of e-commerce transactions |
| |Secure electronic transaction (SET) and /or secure electronic commerce environment (SCCE) services are easily available and |
| |affordable |
| |The telecommunication infrastructure is reliable and efficient |
| |The technology infrastructure of commercial and financial institutions is capable of supporting e-commerce transactions |
| |We feel that there is efficient and affordable support from the local IT industry to support our move on the Internet |
|EER |In general we consider the local environment is ready for e-commerce |
Appendix 5: The Final Instrument
Scale: 1 =Strongly agree, 4= Agree, 3= Neutral, 4= Disagree, 5= Strongly Disagree
I. Awareness
1. Our organization is aware of e-commerce implementations of our partner organizations
2. Our organization is aware of our competitors e-commerce and e-business implementations
3. Our business recognizes the opportunities and threats enabled by e-commerce
4. Our organization understands e-commerce business models that can be applicable to our business
5. We understand the potential benefits of e-commerce to our business
6. Our organization has thought about whether or not e-commerce has impacts on the way business is to be conducted in our industry
7. Our organization has considered whether or not businesses in our industry that fail to adopt e-commerce and e-business would be at a competitive disadvantage
II. Human Resources
1. Most of our employees are computer literate
2. Most of our employees have unrestricted access to computers
III. Business Resources
1. Our people are open and trusting with one another
2. Communication is very open in our organization
3. Our organization exhibits a culture of enterprise wide information sharing
4. We have a policy that encourages grass roots e-commerce initiatives
5. Failure can be tolerated in our organization
6. Our organization is capable of dealing with rapid changes
IV. Technological Resources
1. We have sufficient experience with network based applications
2. We have sufficient business resources to implement e-commerce
3. Our organization is well computerized with LAN and WAN
4. We have high bandwidth connectivity to the internet
5. Our existing systems are flexible
6. Our existing systems are customizable to our customers’ needs
V. Commitment
1. Our business has a clear vision on e-commerce
2. Our vision of e-commerce activities is widely communicated and understood throughout our company
3. Our e-commerce implementations are strategy-led
4. All our e-commerce initiatives have champions
5. Senior management champions our e-commerce initiatives and implementations
VI. Governance
1. Roles, responsibilities and accountability are clearly defined within each e-commerce initiative
2. E-commerce accountability is extracted via on-going responsibility
3. Decision-making authority has been clearly assigned for all e-commerce initiatives
4. We thoroughly analyze the possible changes to be caused in our organization, suppliers, partners, and customers as a result of each e-commerce implementation
5. We follow a systematic process for managing change issues as a result of e-commerce implementations
6. We define a business case for each e-commerce implementation or initiative
7. We have clearly defined metrics for assessing the impact of our e-commerce initiatives
8. Our employees at all levels support our e-commerce initiatives
VII. Market Forces eReadiness
1. We believe that our customers are ready to do business on the Internet
2. We believe that our business partners are ready to conduct business on the Internet
VIII. Governmnet eReadiness
1. We believe that there are effective laws to protect consumer privacy
2. We believe that there are effective laws to combat cyber crime
3. We believe that the legal environment is conducive to conduct business on the internet
4. The government demonstrates strong commitment to promote e-commerce
IX. Supporting Industries eReadiness
1. The telecommunication infrastructure is reliable and efficient to support eCommerce and eBusiness
2. The technology infrastructure of commercial and financial institutions is capable of supporting eCommerce transactions
3. We feel that there is efficient and affordable support from the local IT industry to support our move on the Internet
4. Secure electronic transaction (SET) and /or secure electronic commerce environment (SCCE) services are easily available and affordable
X. eCommerce Adoption
Which one of the following best describes your current e-commerce status? Please choose only one option
1. Not connected to the internet, no e-mail
2. Connected to the internet with e-mail but no web site
3. Static Web, that is publishing basic company information on the web without any interactivity
4. Interactive web presence, that is accepting queries, e-mail; and form entry from users
5. Transactive web, that is online selling and purchasing of products and services including customer service
6. Integrated web, that is the web site is integrated with suppliers, customers and other back office systems allowing most of the business transactions to be conducted electronically
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-----------------------
( Corresponding Author: Alemayehu Molla, IDPM, The University of Manchester, Oxford Road, Manchester M139GH, UK, Tel: +44-161-275 3233, Fax: +44-161-273 8829, E-mail: Alemayehu.Molla@man.ac.uk
[1] An earlier version of the model and its theoretical constructs are discussed in detail in [34].
-----------------------
E-commerce Adoption
POER
PEER
Perceived Organizational eReadiness
Awareness
Resources
Commitment
Governance
Institutionalization
Initial Adoption
If Adopt
Perceived External eReadiness
Government eReadiness
Market Forces eReadiness
Support Industries eReadiness
Governance
G1, G2, G3, G4, G5, G6, G7, G8
Commitment
C1, C2, C3, C4, C5
Awareness
A1, A2, A3, A4, A5, A6, A7
Human Resources
HR1, HR2
Business Resources
BR1, BR2, BR3, BR4, BR5, BR6
POER
Institu-tionalization
Initial Adoption
If Adopt
Technology Resources
TR1, TR2, TR3, TR4, TR5
Market Forces
MFER1, MFER2
PEER
Government
GOVER1, GOVER2, GOVER3, GOVER4,
Supporting Industries
SIER1, SIER2, SIER3, SIER4,
................
................
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