Determinants of website development: a study of electronic
commerce in Singapore
N. Rao Kowtha
a,*, Timothy Whai Ip Choon
baNUS Business School, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260, Singapore bSingapore Telecommunications Limited, Singapore 239732, Singapore
Accepted 5 March 2001
Abstract
The website is the portal through which most of the electronic transactions are conducted today. The site's development provides a glimpse of the ®rm's electronic commerce (E-commerce) strategic objectives. Yet, few studies on E-commerce have related the ®rm's website to its strategy. This paper examined the relationships between the strategic variables of competitive intensity, existing competencies of the ®rm, ®rm size, and strategic commitment on the one hand, and the development of the ®rm's website, on the other. We developed a model based on existing literature in E-commerce and strategy. The study was conducted with 135 ®rms from the travel, ®nancial and information technology (IT) sectors in Singapore. Results show that competitive intensity, ®rm size and existing competencies positively in¯uence the ®rm's strategic commitment to E-commerce. The commitment in turn affects the website development. We also show that websites can be
classi®ed according to their developmental level.#2001 Elsevier Science B.V. All rights reserved.
Keywords: Internet; E-commerce; Website development; Electronic commerce strategy
1. Introduction
Although there has been a rush by most ®rms to establish some presence on the Internet, not all ®rms seem to pursue electronic commerce (E-commerce) with the same vigor. Some surveys suggest that only about 29% of all sites in the population actively engage in basic transactions or more complex exchanges [16]. A cursory inspection of the World Wide Web (WWW) reveals a wide variety of species. Some ®rms have chosen to develop their sites to a high level of sophistication and integration whereas others
appear to be content to maintain mere informational sites over the years. Firms' online business models can be signi®cantly different even within the same indus-try [37]. Some writers have argued that the sophistica-tion and complexity of the ®rm's website re¯ects the strategic priorities of the ®rm since the website is the portal through which most of the electronic transac-tions are conducted [3]. Several authors have also proposed an evolutionary scheme of site development in recent years.
This paper asks two questions. If the website indeed re¯ects the ®rm's E-commerce strategy, how are strategic variables related to the development of a ®rm's website? Can websites be empirically classi®ed on the basis of their complexity and sophistication? The determinants of the ®rm choices for E-commerce have not yet been formally identi®ed. Few authors *Corresponding author. Tel.:65-874-3049;
fax:65-775-5571.
E-mail address: [email protected] (N. Rao Kowtha).
0378-7206/01/$ ± see front matter #2001 Elsevier Science B.V. All rights reserved. PII: S 0 3 7 8 - 7 2 0 6 ( 0 1 ) 0 0 0 9 2 - 1
have linked the ®rm's E-commerce objectives and strategies to the ®rm's website. In this paper, we identify some of the determinants and explore the relationships between these determinants and website development.
A second question pertains to the meaning of website development. Huizingh [26] recently pro-posed a useful framework for distinguishing between the content and design aspects of websites. His frame-work, however, does not classify websites on the basis of this framework. A secondary aim of this paper is to examine if websites can be classi®ed according to their developmental level. An understanding of web-site development and the underlying strategic factors can throw some light on the ®rm's strategic choice and implementation.
Our review of literature on E-commerce (e.g. [62]), the resource-based perspective on strategy (e.g. [7]), ®rst mover advantages (e.g. [31]), entry timing and complementarities (e.g. [24]), and information tech-nology (IT) adoption (e.g. [28]) suggests a few poten-tial determinants of site development. Speci®cally, we investigated the association between ®rm size, website age, ®rm age, competitive intensity, existing compe-tencies and strategic commitment on the one hand, and the development of the ®rm's website, on the other. The study was conducted with 135 ®rms from the travel, ®nancial and IT sectors in Singapore. In the literature review that follows, we ®rst present a brief summary of the relevant practitioner literature on E-commerce to establish our deduction of critical strategic factors. This is followed by an explanation of website development, and the development of a causal model linking the strategic factors to website development.
2. Literature review and hypotheses
There is no consensus in the ®eld over the de®nition of E-commerce. We have adapted a more general usage of E-commerce as any business that is trans-acted electronically, whether the transaction occurs between two business partners, or a business and its customers [13]. This would allow the inclusion of business-to-business (B±B), business-to-consumer (B±C), and business-to-government (B±G) transac-tions.
2.1. Factors affecting the adoption of E-commerce From published accounts of E-commerce, three factors seem to consistently in¯uence the development of E-commerce and websites. Broadly, these factors are the competitive environment, strategic commit-ment of the ®rm and the required competencies.
In the highly volatile E-commerce environment, an aggressive posture, agility and ¯exibility are often said to help the ®rm [67]. Often, competitor moves are either imitated or taken as the benchmark in E-com-merce adoption decisions [19,34]. Thus, the intensity of competition seems to be a catalyst for the ®rm's decision to go online. For instance, Dell's proactive move into E-commerce has put pressure on competi-tors such as Compaq to follow suit not only in the consumer retail market but also in the corporate client segment. Many ®rms, however, are still wary of the numerous unresolved issues of E-commerce transac-tions, such as security, accessibility, and the possible returns on investment [18]. In addition, the rapid evolution in technology has exacerbated the problems associated with adaptation. This could be due to two other related factors: strategic inertia [23,41] and ®rm competencies [52].
Strategic inertia has been witnessed before in the IT sector [53,60], and it holds for E-commerce too. Ash [5] identi®ed lack of management commitment and proper strategic objectives as two of the reasons for Internet project failures. Moreover, the ®rm's current product or business strategy may not allow a diversion to E-commerce with any immediacy, since all pro-ducts do not lend themselves to E-commerce applica-tions [30,59]. The move from conventional channels to the web is also fraught with its own riddles. Managers, for instance, have to evaluate the long run implications and the involved trades-off in replacing conventional channels with a new sales model [21]. In many instances, they will also have to ®nd new reliable partners who are also operating on the web [8,48]. In addition, many ®rms simply lack the competencies to operate an electronic channel.
Critical competencies required for successful E-commerce implementation have less to do with the technology itself. They lie in the managerial domain, comprising supply chain management, value chain identi®cation, management of logistics alliances, and customer-driven enhancement of business features
[10,50]. Many ®rms lack experience in this new medium. However, ®rms, which are already practicing IT-based supply chain management and total quality management will ®nd a switch to Web-based com-merce easier, thus giving them an early adopter advan-tage. Existing procedural knowledge often facilitates a smoother transition to the new technology.
Thus, the dicta for a successful adoption of E-commerce appear very similar to IT adoption in organizations (cf. [42]). Our review of the practitioner and relatively scant academic writing on E-commerce shows that the competitive intensity in the environ-ment, the ®rm's strategic commitenviron-ment, and existing competencies signi®cantly in¯uence the extent of E-commerce adoption at a given point in time. We argue that this is re¯ected in the development of the ®rm's website. While the determinants are apparent, their precise relationship to website development is not clear. For this reason, we relied on strategy and IT adoption literature to derive our model and predictions.
2.2. A model of website development
We present our model ®rst, followed by an explana-tion of the included variables and a substantiaexplana-tion of the postulated relationships. The model is shown in Fig. 1. We propose six drivers that could signi®cantly in¯uence a ®rm's website development and effective-ness. These areprior competencies,competitive inten-sity, ®rm size, website age, ®rm age and strategic
commitment. The relationships between these vari-ables and the criterion variable of site development follow a path model.
The model postulates that competitive intensity, prior competencies, ®rm age and ®rm size in¯uence the ®rm's strategic commitment, which has a mediat-ing effect on website development. The model also shows an independent effect of website age on website development. Competitive intensity, prior competen-cies, website age, ®rm age and ®rm size are the exogenous variables. The endogenous variables in the model are strategic commitment and website development. We discuss the model below, starting with the dependent variable of website development, followed by the other variables in the sequence. 2.2.1. Website development
Website development refers to the technological aspect of ®rms' E-commerce undertaking. The evolu-tion of websites is not yet clearly understood. Various authors have suggested tentative frameworks to clas-sify the sites [65]. Almost all of these classi®cations imply a development of sites from the most basic to the most comprehensive level of sophistication and access. West [65] asserts that such frameworks, because of their speci®city in identifying character-istics of websites, would allow for a proactive plan-ning and allocation of resources and also align website resources with business objectives. Although ®rms can leapfrog several generations to mount a complex site, an evolutionary approach helps in locating a ®rm's site
on a continuum [25]. Fig. 2 shows the Coleman framework, which is the most differentiated of the existing typologies.
Coleman [16] advocates a graduated framework from the ®rst generation sites of ``organization-cen-tric'' and ``intranet entry-point'' type of homepages, to the ultimate ``business-centric'' and ``marketplace integration'' type of delivery platform expansion. The ®rst generation represents the most basic infor-mational site. The second-generation site facilitates data collection and also product catalogues. The third generation website is capable of handling business transactions in addition to information and catalo-guing. The fourth generation provides work¯ow inte-gration. Finally, the ®fth generation, the most advanced, is integrated with the marketplace, provid-ing for intranet and extranet extensions. Successive generations incorporate the features of the previous generations. Thus, a ®fth generation site will be the highest level of development, and the ®rst generation site represents the lowest.
The framework speci®es a link between site devel-opment and business objectives. Thus it imputes a strategic intent behind site sophistication. The frame-work also combines the design and content aspects of websites in contrast to the Huizingh study. An evolu-tionary classi®cation of websites in terms of technol-ogy can help to discriminate the underlying strategic goals and the commitment of ®rms. As there need not always be a match between the stated goals and ®rms' E-commerce implementation, examining the techno-logical features of websites may afford us a clearer picture of ®rms' actions.
2.2.2. Strategic commitment of the management Strategic commitment refers to not only the articu-lation and symbolic championing of a new under-taking by the top management but also the commitment of resources [43]. Many studies in IT have shown that sustained investment in technology and human resources coupled with ardent champion-ing by the top management holds the key to successful IT adoption [49]. Studies in technology adoption also have shown that new ventures that thrive in complex and dynamic environments devote substantial resources to innovation [35,61]. Thus, the strategic commitment of the ®rm deeply in¯uences not only the conceived goals but also the resource commitment and eventual implementation [46].
If website development is indeed a strategic move, it should be causally dependent on the ®rm's strategic commitment. A ®rm that wishes to initiate E-commerce has to be committed not only for the signaling value but also for the substantive realization of its goals [22]. These arguments and ®ndings lead to the deduction that strategic commitment predicts the developmentof websites. Although strategic commit-ment might in¯uence several other strategic and per-formance factors, it could also be driving the development of websites.
Hypothesis 1. The strategic commitment of a firm to E-commerce will bepositivelyrelated to its website's position along the development continuum.
There is some evidence that ®rms select their strategies according to the existing capabilities [14,15]. Thus, current capabilities can signi®cantly in¯uence the strategic commitment of the ®rm towards new opportunities in the environment. Secondly, the competitive intensity in the environment accelerates the adoption of a new technology, entry into emerging ®elds or diversi®cation into new ventures [38]. Thus, the competitive intensity in the ®eld of E-commerce may act as a catalyst for heightened strategic commit-ment, and consequently, the website development. Next, we examine these two exogenous variables as well the role of ®rm size, ®rm age and website age. 2.2.3. Prior competencies
The competencies of a ®rm refer to the coordinated deployment of all its available resources and capabil-ities. In this paper, we de®ned prior competencies as Fig. 2. A representation of website evolution.
anexisting set of procedures and routines, technolo-gies, andexperience with customer groups(cf. [1,20]). Strategy research has shown that ®rms move in the direction set by existing capabilities. The underlying reason appears to be the ease with which the transition into a new ®eld or to a new technology can be made. Technology and entry timing studies have shown that diversi®cation into new ®elds or adoption of new technologies is signi®cantly contingent on comple-mentary resources and existing competencies. This is especially so in a highly competitive environment where the incumbent ®rm's market is threatened, which many envisage the Internet to do to dominant players.
Moreover, managers who are already familiar with IT seem to show a positive bias towards further adoption and extension [33]. On the one hand, such managers have experienced the bene®ts of IT, and therefore, respond positively to further development if there is strategic promise in such adoption. Secondly, similarity in technologies could induce con®dence in the managers with respect to the new technologies and processes. Thus, a ®rm that has relevant competencies for the new environment may demonstrate more stra-tegic commitment to E-commerce than a ®rm, which has no relevant competencies.
In E-commerce, many of the technological aspects can be outsourced. However, some of the critical competencies for the coordination of the increasingly precise operations need to be internally developed. These include the ability to reach and serve remote markets, logistics and tight back-end operations. Thus, we expect ®rms that have managerial, logistic and IT competencies to show a positive commitment to E-commerce and website development.
Hypothesis 2. The degree of prior competencies relevant to E-commerce possessed by a firm will be positivelyrelated to the firm's strategic commitment to E-commerce.
Hypothesis 3. The degree of prior competencies relevant to E-commerce possessed by a firm will be positivelyrelated to the firm's website development. 2.2.4. Competitive intensity
Numerous studies have shown that entry into emer-ging ®elds is accelerated by the competitive intensity
in the environment [29,54]. Mitchell found that incumbent ®rms make an early entry into emerging sub®elds when there is increased rivalry. Other ®nd-ings show increased competitive intensity resulting adoption of green marketing strategies, diversi®cation of community banks [57], and reduced cycle time for new products in technology ®rms [55]. Based on this literature, we argue that competitive intensity plays a signi®cant role in pushing ®rms towards the Internet platform. In further support of our argument, Dos Santos and Peffers [19] found that competitor moves and increasing competition do signi®cantly in¯uence the adoption of E-commerce in many industries. Thus, increasingly intense competition affects the strategic commitment of the ®rm and consequently, the gen-eration development of the website.
Hypothesis 4. The degree of competitive intensity in an E-commerce environment will bepositivelyrelated to the firm's strategic commitment to E-commerce.
Hypothesis 5. The degree of competitive intensity in an E-commerce environment will bepositivelyrelated to the firm's website development.
2.2.5. Firm size
The Internet platform has generally been thought of as the perfect instrument that allows small ®rms to compete with their bigger cousins [44]. However, the lack of resources in the initial stages can sometimes undermine their efforts to acquire the necessary tech-nologies and competencies [63]. Size can also limit the access to the necessary capital. Thus, we hypothe-size that ®rm hypothe-size will be positively related to the ®rm's strategic commitment to E-commerce.
Hypothesis 6. Size of the firm will be positively related to the firm's strategic commitment to E-com-merce.
2.2.6. Firm and website age
Like size, age has often been posited to have a strong impact on resources and performance [2]. As ®rms age, they develop commercially viable routines and become more sophisticated in their operations [39]. In a similar vein, older websites would have given their ®rms more time to learn through the trial and error process and develop the website further [32].
Such an argument assumes that learning does take place with age; therefore, website age is likely to have some impact on the viability of a highly integrated and functional website.
On the other hand, ®rm age is often cited as the reason for conservative orientations due to institutio-nalized routines and power centers. If this is true, then ®rm age should show a negative effect on strategic commitment.
Hypothesis 7. The age of a firm will be negatively related to the strategic commitment of the firm to E-commerce.
Hypothesis 8. The age of a firm's website will posi-tively related to the development level of the firm's website.
This concludes our explanation of the model tested in this study. To reiterate, we argue that a ®rm's existing competencies and the competitive intensity in the environment positively in¯uence the strategic commitment of the ®rm to E-commerce. We also hypothesized that prior competencies and competitive intensity will have an independent effect on website development. The ®rm's size, as a proxy for resource availability, also in¯uences the strategic commitment. Strategic commitment in turn has a positive effect on the level of website development. We also hypothe-sized that website age will not have any effect on website development. In the following section, we present our hypotheses derived from this model.
3. Method
3.1. Measures
Firm size, age and website age were measured with single items. For all other variables, we used multiple item (5-point, Likert-type) scales. We measured stra-tegic commitment with a 12-item scale. In our ques-tions, we tapped into both the strategic orientation of the chief executive and resource commitment. Our items on orientation were adapted from Covin and Slevin [17], and we developed the items for resource commitment. Sample items included, ``We have already made signi®cant dedicated investments to develop E-commerce competencies'', ``Personnel in
our company are always updated and trained in the newest technologies'', and ``We have to date signi®-cantly increased our website's capabilities to handle entire E-commerce transactions''.
Based on Abell and Durand, we tapped into the capabilities that would predispose ®rms towards E-commerce. In all, ®ve items measured the respon-dent ®rms' competencies prior to engaging in website development. The items investigated if respondents had any prior experience with or procedures-in-place for, IT, serving remote customers and supply chain management. Higher scores on this scale indicate higher degrees of relevant prior competencies. We used four items adapted from Covin and Slevin [17] to measure competitive intensity. Firm size was mea-sured as the natural logarithm of the total number of full-time employees in the ®rm [58]. We also took the natural logarithms of website and ®rm ages in months, to normalize the distribution.
To determine the position of a website on the generation continuum, we proceeded in three stages. First, 20 items were developed using items from Cole-man [16], Mukherjee [40], Stroud [59], and West [65]. These items were referred to three experts at a local IT research institute. Their suggestions resulted in decomposing the 20 items to 25 items to afford clarity. Items dealing with role-based work¯ows, customer support and integrated website were modi®ed for this purpose. All the items of this scale appear in Table 2. Prior to the survey administration, we pilot tested all the scales with 25 students enrolled in a MBA E-business course at a local university. Our purpose was to test the relevance of the items, and context-sensitivity of language. The subjects are working students, with a mean age of 31 years and mean work experience of 9.6 years. All of them hold managerial or engineering positions. Twelve of the respondents worked in the IT industry, and three of these are E-commerce managers in their ®rms. All of them are familiar with E-commerce. Thus, this was a knowl-edgeable target for the pre-test. We also asked the students to examine the website development items for technical and substantive accuracy. Their suggestions served to improve the wording of the questionnaire.
The pre-test of the website development scale yielded four factors instead of the expected ®ve. These factors, however, were clear-cut. On the other hand, reliabilities of the measures for strategic commitment
(0.81), prior competencies (0.66) and competitive intensity (0.60) were acceptable. Although this is not a rigorous pilot test, we note that all measures have been adapted from previously published studies, with the exception of website development questions. For this latter set of items, we took care to establish some content validity through the usage of experts and knowledgeable subjects.
3.2. Data collection
We obtained the data for this study through an on-line survey of ®rms in Singapore. We drew our sample from the WWW itself. We conducted the study with ®rms in Singapore from the travel, ®nance, and IT industries. We chose these three industries because the product and service offerings share many character-istics, and also are the fastest developing sectors of the E-commerce domain. We used the classi®cation of companies as listed in the search engine, Yahoo!, to identify 334 companies from a total of 380 ®rms listed on the engine. We eliminated 46 defunct ®rms. We also used other engines and portals to ensure that we captured all the ®rms.
The categorization by search engines does not follow the SEC classi®cation of industries, and there are frequent overlaps such that the same ®rm and website can be found under different categories. How-ever, Yahoo! provides some face validity for the classi®cations by using a method similar to the Delphi technique [64]. A professional team of evaluators from a wide spectrum of disciplines including practitioners and academics are engaged to ``surf'' sites that are submitted by Webmasters or site owners. These sur-fers decide about the inclusion and classi®cation of the new site. They are allowed to change their decisions later, but the feedback and window period is consider-ably shorter than a Delphi technique (Mr. Maury Zeff, Business Manager, Yahoo!Asia, 25 August 1999, personal communication).
An online questionnaire was developed and posted on the local university server. Survey research is prone to common method variance problems, and obtaining data from multiple respondents could alleviate the problem to some extent. However, in the Singapore context, obtaining the participation of informed multi-ple respondents from every participant ®rm is dif®cult. We anticipated the problem and took steps to alleviate
it. First, we contacted the ®rms in the sample to ®nd out the names and designations of the CEOs and the persons in charge of the electronic channels. In the case of many smaller ®rms, the two positions coin-cided. Where possible, we invited the senior manager in charge of the electronic channels and the chief executives to participate in the survey. The electronic channel managers answered the IT related site devel-opment questions whereas the chief executives answered the strategic questions. In the event of no differentiation, the chief executive answered all the questions. Concerns over the validity of responses are somewhat attenuated by the recent ®nding that busi-ness and IT manager perspectives on strategy formu-lation are largely convergent [12].
We also arranged the items of the questionnaire in a jumbled manner to minimize the response bias [47]. In the ®rst phase of our survey, we sent introductory E-mails to the managers of the 334 ®rms, explaining the purpose of the study and requesting the ®rm's participation. Two follow-ups were made over the next 2 weeks. At the end of the survey period, a total of 135 usable responses were obtained (40.4%). Of these, 65 are from the IT sector (48% response rate), 31 from the ®nance category (23%), and 39 from the travel indus-try (29%). Thus, the sample shows a clear skew towards the IT industry.
4. Analysis and results
Of the 135 companies who responded, a majority (71.8%) employed less than 100 full-time employees. Thus the sample appeared to be slightly skewed towards the small size (median32:0), which is not surprising given the abundance of Internet start-ups. Table 1 presents the descriptive statistics, corre-lations and reliabilities. The exogenous variables show statistically signi®cant but acceptable levels of corre-lation. The reliabilities of strategic commitment (a0:90) and prior competencies (a0:70) were well above the acceptable level. Competitive intensity had a relatively low reliability (a0:60).
4.1. Website development
The 25 items in the questionnaire were factor analyzed using principal component analysis with
varimax rotation. This yielded four factors, thus sug-gesting some support for the West classi®cation of four generations. Results of the factor analysis are shown in Table 2.
Factors 1 and 3 have three items, and factor 2 has ®ve items. The fourth absorbed 14 items. These 14 items, however, indicate greater technological com-plexity than the remaining 11 items, thus justifying the view that they represent the highest level of develop-ment. For instance, factor 1 had three items that enquired about simple informational sites, while fac-tor 4 had 14 items that enquired about websites that were technologically advanced and capable of supporting Web-based transactions. Similar interpre-tations were then extended to the remaining factors.
We used the four factors to represent the four levels of website development. However, the content of the factors did not precisely ®t the criteria suggested by Coleman [16] shown in Fig. 1. We obtained only four factors as opposed to the ®ve proposed by Coleman [16]. For this reason we labeled the factors as follows: generation 1 (information and catalogue), generation 2 (database), generation 3 (basic transactions and search), and generation 4 (integrated site). Table 2 shows the labels for each factor based on the content. Essentially, the hypothesized fourth and ®fth factors collapsed into one factor. The integrated site (genera-tion 4) is the highest level of development captured in this sample and generation 1 (information and cata-logue) represents the basic site.
One assumption underlying the classi®cation is that for a website to possess higher generation features, it should also logically have the lower generation char-acteristics. Thus, the total generation development
for a website is the additive score from the four factors. We tested this assumption of continuity and additivity by comparing means and by using the squared Euclidean distance as the measurement. We calculated the ®rm's score for each generation by averaging the responses in each factor and summing the four averages. This score represents the website develop-ment score for the ®rm.
Prior to further analysis, we selected 45 sites from the responding 135 sites. We scanned these sites to the extent allowed by the site's security features. We wished to con®rm that each site has capabilities and features that matched the responses of the ®rms. The responses could be biased either way, with some ®rms indicating less than their true capabilities, and others, overestimating the features. Our scanning showed that this problem is likely to be minimal among the respondents.
4.1.1. Tests for generational development
Although factor analysis provides an indication of the number of dimensions, it does not indicate the evolutionary pattern. Tests for evolutionary order of this nature have been scarce. Earlier studies on orga-nizational life cycle [36] were content to show a difference between the stages of life cycle by compar-ing within-group differences with between-group dif-ferences. But they did not show that the life-cycle stages arise in the order claimed by theory; rather, theory was something that was taken for granted. The task at hand is similar to a life-cycle analysis with the additional claim of an evolutionary order. We have conducted three different tests to ascertain the order of generations.
Table 1
Descriptive statistics and correlationsa
Variables Mean S.D. 1 2 3 4 5 6 7 Firm age 4.67 1.17 1.00 Firm size 4.13 2.18 0.55** Website age 3.02 0.94 0.15 0.16 Prior competencies 3.34 0.82 0.21* 0.28** 0.094 (0.70) Competitive intensity 3.34 0.70 0.03 0.09 0.09 0.34** (0.60) Strategic commitment 2.78 0.78 0.13 0.36** 0.08 0.56** 0.55** (0.90) Website development 12.6 3.15 0.02 0.25** 0.12 0.53** 0.56** 0.83** ±
aReliabilities are shown in the diagonal. *P< 0.05.
Firstly, in order to test if there is a discernible pattern, we measured the squared Euclidean distances between the generation vectors. We selected this measure based on the relational demography research, which assesses similarity between individuals using an analog of Euclidean distance [45,66]. The squared Euclidean distance is a measure of dissimilarity, with larger distances indicating more dissimilarity [51].
Our premise is that there is a progressive relationship from generation 1 to generation 4. The distance between generations 1 and 4 scores should thus be greater than any other distance, while the distance between generations 1 and 3 should be the next great-est, and so on.
Table 3 shows these squared Euclidean distance and they all suggest a progressive effect, except that Table 2
Factor analysis of website development measuresa,b
Questionnaire items Generation 1
(information and catalogue)
Generation 2
(database) Generation 3(basic transaction and search)
Generation 4 (integrated site)
Provision of information 0.817
Relational database and search (SQL) 0.453
Website not used for transactions 0.568
Catalogue of products and services 0.789
Functional database and search functions 0.656
Updates on products and services 0.538
List of potential clients/partners from Web-``hits'' 0.731 Database of customer/partner information through the website. 0.740 Customer support technology (e.g. CRM) 0.565 Feedback from customers through website 0.561
Website allows customer service activities 0.568
Website supports portal technology 0.736
Website permits business transactions 0.755
Able to deliver through the website itself 0.717
Website is capable of only taking orders 0.751
Website supports secure payment technology 0.641
Firm uses role-based workflows 0.706
Firm uses ERP 0.777
Website supports extranet activities 0.672
Website links end-users, partners and suppliers 0.586
Website combines intranet, extranet and Internet 0.649
Website has self-service downloads of programs and applications 0.683
Firm integrates technical support with customer support 0.506
Website has multimedia and other features 0.575
At the point where E-commerce through the website is the
main source of sales 0.758
aOnly loadings greater than 0.40 are shown.
bCumulative variance explained by the four factors67%.
Table 3
Squared Euclidean distances among the four generations
Generation 1 Generation 2 Generation 3 Generation 4
Generation 1 0.00 192.2 305 398.4
Generation 2 192.2 0.00 121.7 114.7
Generation 3 305 121.7 0.00 96.7
generation 2 is almost equidistant from generation 3 (121.7) and generation 4 (114.8). This could be due to random or measurement errors. Admittedly, the test is not robust, and should not be taken as the ®nal word on an evolutionary effect. It, however, provides some initial indication of websites' generation development order, in conjunction with the factor analysis results. The second test involved a paired samplet-test on the absolute scores of the various generations. For a given ®rm, by the evolutionary argument, scores of lower generations websites should be greater than or equal to the scores of higher generation. This is because ®rms would generally have to saturate their capacity for lower generations before moving to higher generations. Lower generation scores should then be signi®cantly higher than high generation scores. Our results support this notion (Table 4). All mean differ-ences are positive and statistically signi®cant.
Finally, we carried out a second paired sample t -test, using differences. If the generations are indeed evolutionary, then the difference in scores between generations 4 and 1 (labeled diff41) should greater
than or at least equal to the difference between gen-erations 3 and 1 (diff31). Since generation 4 is the last one to be developed on the website, its score should be less than or equal to generation 3 score and generation 1 score. Similarly, generation 3 score should be less than or equal to the scores of generations 2 and 1. Thus, diff41 should be greater than diff31. This implies that generation 4 would be furthest from generation 1, followed by generations 3 and 2.
The results of the test are shown in Table 5. Pair 1 (diff41±diff43), for instance, has a signi®cant positive mean (M1:197, t15:26, P<0:001). This implies that the difference between generations 4 and 1 is greater than the difference between genera-tions 3 and 4. The result is in the hypothesized direction. Similarly, pair 4 (diff41±diff31) is also signi®cantly positive, indicating that the scores of generations 3 and 1 are closer than the scores of generations 4 and 1. Thus, the result is in the hypothe-sized direction. This holds for all possible six pairs as shown in the table. This shows some support for the argument of a gradation effect.
Table 4
Thet-tests of scores between generationsa
Pairs Paired differences t d.f. Significant (two-tailed)
Mean S.D. Gen1±gen2 0.87 0.82 12.39 134 0.000 Gen1±gen3 1.19 0.91 15.26 134 0.000 Gen1±gen4 1.49 0.85 20.42 134 0.000 Gen2±gen3 0.33 0.90 4.22 134 0.000 Gen2±gen4 0.62 0.68 10.59 134 0.000 Gen3±gen4 0.30 0.80 4.33 134 0.000
a``Gen'' denotes the generation number.
Table 5
Thet-test of difference in scores between generationsa
Pairs Paired differences t d.f. Significant (two-tailed)
Mean S.D. Diff41±diff43 1.197 0.9118 15.26 134 0.000 Diff41±diff42 0.872 0.8175 12.39 134 0.000 Diff31±diff32 0.872 0.8175 12.39 134 0.000 Diff41±diff31 0.297 0.7958 4.33 134 0.000 Diff31±diff21 0.325 0.8955 4.22 134 0.000 Diff21±diff41 0.622 0.6826 10.59 134 0.000
Summarizing the tests, there is some evidence for the existence of a graduated pattern of website devel-opment. Measures of the squared Euclidean distance, the t-tests and exploratory factor analysis lend some strong support to this argument.
4.2. Path analysis and model testing
Our small sample size and relatively large number of variables do not allow a measurement model [9]. However, we could conduct path analysis with observed variables, which is used apart from the mea-surement model, focusing only on the a priori hypothe-sized relations between observed variables [27]. The path coef®cients obtained through a structural equation program are the same as those obtained from a regres-sion-based path analysis. Moreover, using a structural equation program facilitates the detection of under- and over-identi®cation of the model. It also allows a con-venient evaluation of correlations between the error terms and exogenous variables, which is not permis-sible in a path model.
There are ®ve exogenous variables in this study: prior competencies, competitive intensity, ®rm size, ®rm age and website age. The endogenous variables are, strategic commitment and website development. In the tested model, we established linkages between prior competencies and website development, and also competitive intensity and website development. This would facilitate the detection of partial or full media-tion by strategic commitment. We used the SPSS AMOS4 package to conduct the path analysis. AMOS provides maximum likelihood estimates of paths, several measures of ®t, and modi®cation indices that can help in model identi®cation. We conducted a preliminary check for correlations of residuals with the exogenous variables. The modi®cation indices provided by the output show if there are any such signi®cant correlations [4]. There were no signi®cant correlations; nor was there a signi®cant departure from normality for any variable.
The model represented a good ®t as indicated by the w2statistic (4.7, d:f:3,P<0:20). Apart from thew2
statistic, the ®t indices also indicated a satisfactory ®t. The goodness-of-®t index (GFI) stood at 0.99 while the AGFI was 0.91. The comparative ®t index stood at 0.99. Thus, all the obtained ®t measures were at or above the acceptable levels. The root mean square
error of approximation (RMSEA) was 0.064. The RMSEA is a robust measure of ®t, and estimates exceeding 0.1 indicate a poor ®t [11]. These measures indicated a just-identi®ed model with a good ®t.
The model is able to estimate 51.1% of the variance in strategic commitment and 71.7% of the variance in the development of the website as indicated in the estimates of squared multiple correlations from the AMOS output. Competitive intensity has a total effect of 0.44 on strategic commitment and 1.88 on website development. Prior competencies has a total effect of 0.35 on strategic commitment, and 1.3 on website development. Firm size has a total effect of 0.10 on strategic commitment. Strategic commitment has a total effect of 2.84 on website development.
We now turn to the path coef®cients and tests of hypotheses. Fig. 3 shows the standardized path coef®-cients. We present the standardized path coef®cients to allow comparison between the exogenous variables since they are measured in different units [6]. The signi®cance of a path coef®cient is given by its critical ratio which is the parameter estimate divided by its standard error estimate (thez-score).
4.2.1. Tests of hypotheses
The path coef®cient for the effect of prior compe-tencies on strategic commitment is 0.37, signi®cant at the 0.001 level (CR5:5). It indicates a substantial positive in¯uence of prior competencies on the extent of commitment to E-commerce. The path coef®cient for competitive intensity to strategic commitment is 0.40, signi®cant at the 0.001 level (CR6:16). This indicates again a substantial and positive effect of competitive intensity on strategic commitment. In other words, ®rms in more competitive and less muni®cent environments appear to have more com-mitment to E-commerce. Firm size is also positively related to the strategic commitment (b0:28, CR3:8, P<0:001). Hypotheses 2, 4 and 6 are thus supported.
As predicted by Hypothesis 1, strategic commit-ment shows a substantive and highly signi®cant effect on website development (b0:71, CR11:3, P<0:001). We also note that competitive intensity has a direct effect on website development (b0:14, CR2:5,P<0:05). Prior competencies do not show any such direct effect on website development. Thus, strategic commitment fully mediates the relationship
between prior competencies and website develop-ment. With respect to competitive intensity, the results indicate partial mediation. Hypothesis 5 is supported but Hypothesis 3 is rejected. Firm age does not show a signi®cant relationship to strategic commitment (b 0:11, CR 1:5, P>0:1). Website age is also not signi®cantly related to website development (b 0:05, CR 0:96, P>0:1). Hypotheses 7 and 8 are thereby rejected. We discuss these results as follows.
5. Discussion
This study has attempted to understand the primary drivers behind the development of websites. We have also evaluated the evolution of websites. We found some evidence that websites can be identi®ed along four dimensions depending on their level of sophisti-cation and integration. The ®ndings also show that prior competencies, competitive intensity and ®rm size substantially in¯uence the strategic commitm-ent of a ®rm to E-commerce. Strategic commitmcommitm-ent and competitive intensity affect the website develop-ment. Firm age and website age show no relationship to any of the endogenous variables. Our results do not speak for the success of the responding ®rms in
E-commerce. Rather, they only indicate the extent of relationship between certain strategic variables and website development.
5.1. Evolutionary development of websites
Our tests showed some support for the evolutionary arguments. We found four clear factors with an indi-cation of progressive dissimilarity and proximity. We are not aware of any robust tests for such claims. Nevertheless, the results show that website develop-ment has distinct technological dimensions. While the number and notion of generations of website devel-opment remains contestable, using such a scheme should enable researchers to locate the match between the ®rm's strategic posture and its true realization of E-commerce objectives.
From a practitioner perspective, clear strategic goals and commitment are more important than just website development to succeed in this new domain. This is clearly re¯ected in the strong relationship between the articulated commitment of the manage-ment to E-commerce and the developmanage-ment of the website. The website should facilitate strategy imple-mentation. A mismatch between the ®rm's objectives, resource allocation and its web presence is a common occurrence in this evolving ®eld.
Fig. 3. Standardized parameter estimates of the modi®ed path model (the bold, underlined ®gures are signi®cant main effects; variances and error terms are shown inside the boxes; covariances are shown in parentheses).
An interactive and complex website requires sig-ni®cant dedicated investments, skill acquisition and management commitment. Although E-commerce between ®rms has been in vogue for more than a decade, the advent of Internet has shifted many of these transactions from dedicated channels to the more generic Web-based extranet and intranet [56]. For example, from a technological viewpoint, some of the physical additions or modi®cations to a website would include back-end logistics and front-end cus-tomer service integrative technology. A highly devel-oped site should be extensible and have room for subsequent business expansion and changes in the Internet protocol. Hence, the website's strategic importance.
Our emphasis on the strategic signi®cance of web-sites must be tempered with the potential impact of two factors. One is the industry effect and the other, the bandwagon effect. It cannot be assumed that all ®rms would want to pursue a highly sophisticated form of E-commerce at this point in time. Many ®rms in several sectors of Asia are either not prepared or unwilling to readily enter the fray. The website devel-opment score, as calculated here, shows a mean of 12.6 and a near normal distribution. However, the sectors examined are the most conducive to E-com-merce. In the case of IT industry, web designers and solution providers can deliver their products without much physical infrastructure. The same can be said for the travel industry, where services such as reservations require only the IT infrastructure, and the ®rms func-tion mostly as third party intermediaries. Singapore also has a relatively sophisticated regulatory environ-ment that is aimed at facilitating online trading and ®nancial services. Thus, the ®ndings are not surprising with respect to these three sectors.
It is, however, unrealistic to extrapolate to other industries from here. The manufacturing sector, for instance, has to rely on a reliable regional and global logistics network and also has to develop internal competencies to tackle the new environment. The manufacturing sector in Southeast Asia is not yet ready for this task, notwithstanding the extensive infrastructure provided by the Singapore government. In such an event, a ®rm is better off having a minimal informational site.
We also found that competitive intensity had a signi®cant effect on the technological sophistication
of the website. The direct effect of competitive inten-sity could indicate either a counter-move by the respondent ®rms or a bandwagon effect. With rival websites appearing at an alarming rate, ®rms need to ensure that their sites are distinct and value adding. However, ®rms may also perceive that a strong pre-sence on the web will drive away the demons of competition, even if the ®rm is not capable of utilizing the website's true capabilities. These possibilities have to be further investigated.
A note of prudence is appropriate at this point. It is not clear if the factor structure for website develop-ment will remain stable with changing sample types and sizes. Secondly, technologies are evolving very rapidly such that assertion over the number of dimen-sions will be foolhardy. Tests such as the squared Euclidean distance can only be considered as a pre-liminary establishment of the possibility of evolution. 5.2. Competencies, competitive intensity and ®rm size
Our ®ndings lend weight to the idea that strategic orientation towards new technologies and environ-ments is often based on the ®rm's existing skill-sets and capabilities. One implication of our ®ndings is that despite the ease with which a complex site can be mounted, ®rms can ¯ounder. Firms that have the skills and wherewithal also have the con®dence to enter the new environment. In other words, having a complex site is not the answer to E-commerce but having the competencies to use it. The results also reinforce the notion that greater competitive intensity results in increased commitment to a threatened market. E-commerce represents a sub®eld since it does not necessarily displace the products or services that have been traditionally offered but alters the medium of transaction. This allows new entrants into the market through lowered geographical barriers and increased access to customers. This will in turn prompt the established players to up the ante. Thus, established ®rms as well as new entrants will be crowding this new space before any consolidation.
The ®ndings on size are consistent with the entry timing literature; the larger ®rms exhibit more commitment, refuting the media hype that smaller E-commerce start-up ®rms are more capable of responding quickly to the ¯uctuations. A plausible
explanation lies in interaction effects, which are not tested here. When we view size in conjunction with existing capabilities, competitive intensity and entry timing into a new ®eld, size favors early entry. Larger ®rms frequently enjoy quicker access to capital and skills, and have some in-built capabilities. This may be consequential in the case of E-commerce. Unfami-liarity with the necessary practices and technologies is likely to induce a conservative orientation. Thus, a ®rm's existing competencies that are relevant to E-commerce, the competitive intensity in the environ-ment, and the ®rm's size are the most likely drivers of the ®rm's commitment to E-commerce and its website development. Strategic commitment translates into website development.
With regard to website age, it is probable that the liability of newness thesis is not applicable to web-sites. Firstly, new entrants can easily leapfrog several generations of development. Secondly, the develop-ment of viable routines may be more important than age per se for survival rate and success. The relation-ship between website age and learning may not be monotonic. This is particularly true in evolving mar-kets with new technologies. On the other hand, ®rm age does not appear to deter ®rms from adopting E-commerce.
5.3. Limitations and future research
The most obvious limitation to this study has been the small sample size in relation to the number of parameters, although the response rate is reasonable. This precluded a test of the measurement model and validation of the measures. Also, this paper is a static view of the constantly evolving Internet. The sample is drawn from Singapore, which has actively experi-enced E-commerce much later than the United States and Europe. The picture could change with experi-ence. Future research should focus on the longitudinal aspects of website development to present a more dynamic picture of this environment.
It has also been noted that ®rms dealing in cate-gories of products or services ®nd it easier to transfer their operations onto the Internet. Some products or services might be more conducive for marketing on the web, and this could affect the performance and goals of a website. This study did not test for the effectiveness or performance of the websites. We only
established the relationship between size, competen-cies, competitive intensity, strategic commitment and website development. Assessing the performance of a website requires careful identi®cation of the website goals and associated measures. Investigations of the relationship between strategy, website development and performance can produce more conclusive state-ments about the strategic importance of websites.
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