Designed for Success – Empirical Evidence on Features of Corporate Web Pages

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Designed for Success –

Empirical Evidence on Features of Corporate Web Pages Nils Madeja, Detlef Schoder

WHU, Otto-Beisheim-Graduate School of Management, Chair of Electronic Business {Nils.Madeja|Detlef.Schoder}@whu.edu

Abstract

We investigate how eight concepts derived from the media characteristics of the WWW impact corporate success in E-Business if implemented as features of companies’ web sites. We construct a path model for testing our research hypotheses on three subsets of a representative survey of 1,308 cases, 469 general-, 215 which target businesses (B2B), and 224 companies which target consumers (B2C). We find that information- and functionality richness as well as keeping the site up to date are the key drivers of success for general companies.

The key success factors for B2B-companies appear to be the interactive character of the site as well as keeping it up-to-date. B2C-companies can increase their success in E-Business if their web sites show content from a variety of media as well as if they are easy to navigate and readily accessible.

1. Introduction

The World Wide Web exhibits specific media characteristics or so-called “web features” that distinguish it from other types of media (e.g. TV, radio or newspaper). Among others, these media characteristics include technical features (such as accessibility and availability issues), content-related criteria (e.g. media richness and immediacy), as well as features related to user interaction (e.g. personalization or ease-of-use).

It is often reasoned that in order to be “successful”, companies should make appropriate use of the web features for their web sites. However, as corporate web sites can be viewed as a technical- or communications interfaces, they are mostly evaluated under technical- or other aspects of very limited conceptual breadth, with which “success” is measured, e.g. design, usability, features, acceptance of web pages, trust aspects, customer satisfaction, etc. (e.g. [4], [17], or [22]). Also related research on web features and web sites has often focused on certain markets, industries or business models (e.g.,

[3] or [9]). Much of this research has been undertaken in the context of ECCRM (Electronic Commerce Customer Relationship Management, [15]). There has been only little work on how, and if at all, the implementation of measures on companies’ web sites in order to make use of the above web features¹ contributes to corporate success in general and from a comprehensive perspective, e.g., [10]. Therefore, the overall business benefit or business value from implementing web features on companies’ web sites remains difficult to assess, especially for corporate decision makers.

We will address this research gap, investigating if the web features can be viewed as explanatory factors for the success of a company if implemented properly on that company’s web site. We develop a research model which is then verified with empirical data collected in a large- scale survey with originally 1308 cases in the German- speaking market, which is one of the key international E- Business markets.

The remainder of this article is structured as follows:

The research model is developed in the next section: First, the objective of this paper is defined. Then, the web features are introduced, and findings from related literature is mapped onto our list of features. Finally, the research hypotheses are derived. In the third section, the survey, the numerical model and the results of the statistical analysis are presented. In the fourth section, we interpret the results and discuss the findings, contributions and shortcomings of our research. Finally, we derive implications from our work for further research and for practice.

1 From now on, when mentioning web features in the context of corporate web site, we will also refer to “implementing measures on companies’ web sites in order to make use of the media characteristics of the WWW (or web features)” shortly as “implementing web features on companies’ web sites.”

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2. Research Model

2.1. Research Aim and Model Perspective

The aim of our research is to investigate the role of the web features as explanatory factors for corporate success in general and from a comprehensive perspective. We intend to study these relationships in two steps: In the first step, we analyze the general case of companies, without differentiating between any particular industry or company size. In the second step, we concentrate on companies primarily operating in the B2B-segment (B2B-companies) and, separately, on companies primarily operating in the B2C-segment (B2C- companies).

Therefore, we must assume an integrated perspective and choose the corporate level as the level of analysis for our model and, hence, the whole company as the object under study. Consequently, we assume a comprehensive view of corporate performance as the result of Internet usage, not merely concentrating on customer loyalty, - retention, brand awareness or the like. However, although there certainly may be factors which have more impact on corporate success in E-Business than the features that characterize a company’s web sites (or which might even be the cause for the implementation of these features), we limit the explanatory factors in our model to the web features.

Viewing the implementation of web features on companies’ web sites as investments in e-commerce technology, the payoffs from these investments can be assessed qualitatively once the effectiveness of a single web feature or of the web features as a whole for corporate success has been determined. Similarly, conclusions can then be made with respect to the economic value of corporate web sites.

2.2. Web Features – The Media Characteristics of the WWW

For the purposes of our research, we employ the web features as they have been listed in [16], pp. 114-116.

They have been extracted from earlier conceptual work on the use of the WWW and Electronic Commerce, such as [6] and [7].

We exclude 2 factors listed in [16], interoperability (direct and transparent integration of customers’ IT infrastructure with one’s own) and ubiquity or

“pervasiveness” (geographical omnipresence of the Internet as an access medium), since they constitute features which companies cannot control sufficiently by

themselves or in the general case. The remaining 8 web features for our model are:

1. Interactivity – possible bidirectional communication of customers and their suppliers and in particular, the existence of a feedback channel from customers to their suppliers, via integrated services such as e-mail or internet telephony,

2. Immediacy – the opportunity to transmit and receive content as well as to update and react to new content without any significant delay,

3. Connectivity – the formation of collective interactive spaces for communication and collaboration, e. g. in terms of so-called community functionalities or discussion forums, 4. Media Richness and -Variety – the opportunity

to link different types of information, e. g. text, graphics, audio, pictures, and video and display them together as a multimedia object,

5. Availability – the temporal omnipresence of the Internet as an access medium,

6. Information Richness – the opportunity to compile different types of content and content of different quality,

7. Ease-of-Use – users’ ability to browse multimedia content and distributed information transparently, i.e. without being involved in technical or organizational aspects of accessing distributed information, using dedicated communication- and navigation services, and

8. Individualization and Customization – the opportunity to compose individual information or content according to individual preferences of user profiles.

These eight features are considered sufficient for characterizing web sites because they cover a broad range of theoretical dimensions and the main factors governing user interaction with web sites: technical criteria, single- user applications, multi-user applications, content and functionalities as well as multimedia aspects.

2.3. Review of Selected Related Literature

Armstrong and Hagel make a very strong statement in favor of online communities and, thus, the above

“connectivity” feature, stressing the importance of online communities for customer loyalty and showing four sources of value generation within communities [2].

Trepper points out the importance of content currency for website success, thus supporting the assumption that

“immediacy” is a success factor [21]. Similarly, Nielsen and Norman highlight the importance of ease-of-use [14].

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Some of the web features and their applicability for internet marketing are also explained in [19], pp. 103 f., and, in more detail, in [20].

Liu and Arnett derive 6 criteria for the design quality of web sites (information quality, learning capability, playfulness, system quality, system use, and service quality), which they implement as broad multi-item measures [13]. They find support for four success factors of web sites, (quality of information and service, system use, playfulness, and system design quality). However, essentially, their definition of website success is also narrow and focused on customer satisfaction and -loyalty.

Koufaris et al. study the effects that the features of a B2C-commerce site have on shopping enjoyment, and thereby on customer retention and repatronage decisions [12].

Finally, Böing presents an investigation with a similar research methodology and approach as ours [5], pp. 192- 198. He investigates the impact of design elements of B2C-webpages on overall corporate success (which he operationalizes as a single score value). Böing hypothesizes two design elements, the web feature

“immediacy” (measured directly with one indicator variable recording the update frequency) and a complex construct he terms “integrated elements”, comprising a subdimension called “additional information”, a subdimension for transaction-related functionalities, and a third one composed of the two web features

“connectivity” and “individualization and customization”. Although numerical support for the influence of “integrated elements” on corporate success is obtained, no support can be found for the hypothesis that the web feature “immediacy” is also a success factor.

2.4. Derivation of Hypotheses

Considering the literature review, we now generate 8 hypotheses from the web features as listed above by assuming in each case that utilizing the respective feature by making a corresponding implementation either directly on corporate web sites or in the context of corporate web sites (e. g. site management or -operation, related business processes etc.) positively impacts overall corporate success in E-Business.² In every case, we argue that through the use of a particular feature, corporate web sites become more efficient interfaces in the sense of the media characteristics of the WWW, which leads to more user interaction, more business transactions and,

2 In the following discussion, we will use the terms “positively impacts”,

“positively correlates with” or “increases” synonymously. Further, for

“corporate success in E-Business”, we will interchangeably employ the terms “corporate success”, “corporate performance”, or “company performance”.

therefore, to increased success in E-Business for the respective company.

3. Method

The research model comprises the 8 hypotheses as presented above, each hypotheses a single factor influencing the dependent construct of corporate success in electronic business. It is implemented as a path model and tested with numerical data obtained from a large- scale survey. After global model fit has been assessed, the numerical results are evaluated as to if they give support for the single hypotheses.

3.1. The Survey

The numerical data used in the statistical analysis of this model has been collected in a large survey that was conducted from May to June 2000 and which has been published as the “e-reality 2000 study” in September 2000 [18]. This survey was targeted at decision makers of companies in the German-speaking area (Germany, Austria, and Switzerland). To gather data, market research professionals conducted personal interviews with upper- to top-level executives from 1308 companies.

The companies for conducting the interviews were explicitly selected according to a superset of company data, such as to render the survey representative with respect to general company size and industry in the German-speaking market. In case that an interview could not be conducted as planned, a replacement was determined from the same superset in order to maintain the representativity of the sample.

3.2. Aggregation of Survey Data

Prior to the statistical analysis, the gathered raw data is reduced and condensed to an essential subset as follows: At first, we concentrate on companies who had a web page online at the time of the survey, reducing the original data set of 1308 cases to 730 cases (or 55.8%).

(Another 171 companies, or 13.0%, were still planning to launch their site within the next 12 months.) In a second step, we focus on companies who specified that they had yet gained sufficient online experience such as to provide information on the success of their company’ s electronic business activities, leaving a total of 469 valid cases for the numerical analysis.

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Table 1. Operationalization of constructs and measurement of indicator variables.

Construct Operationalization abbreviated wording of the

indicator variable(s) measurement scale inter-

activity binary variable “personal communication, e.g. via web call centers”*

checkbox: checked = “1”, unchecked = “0”

immediacy ordinal scale from 1

through 7 “How often do you update your web site ?”

list of checkboxes, translated into ordinal scale:

“several times a day” = “7”, “daily”

= “6”, “about once a week” = “5”,

“about once a month” = “4”,

“irregularly” = “3”, “hardly ever” =

“2”,“update not necessary” = “1”

connec-

tivity binary variable “we provide functionalities that bring communities together (e.g. chats)”*

media richness and variety

score obtained as the unweighted sum

of checked binary items

“With what kind of companies [not: how many]

does your company cooperate in order to present attractive content to your customers?”

list of checkboxes:

“news agencies”, “advertising networks”, “printing companies and publishers”, ”radio-/ TV-stations”,

“telecom companies”, “other companies in our industry”, “other, please specify:”

availability binary variable “short download times and 100% availability of the web site”*

Q: “How do your company’s online services differ from its conventional offerings?”

A: “The services we offer online are provided with detailed additional information.”

“edited additional content for a product (e.g.

application notes, reported experiences)”*

“further links to external sources of information about our products (e.g. product tests)”*

“service products such as cost calculators or product configurators”

“additional functionalities/ value-added services (e.g. real-time stock quotes)”**

information richness

of checked binary items

“value-added services via mobile access (e.g.

cell-phone applications via WAP”**

ease-of-use binary variable “very user-friendly design of the web sites (‘three clicks to purchase’)”*

“individualized information and services through personalized web sites or e-mails”*

“web site personalization according to customer’s preferences (‘personalized web site’)” **

individualization and customization

of checked binary

items “individualization of online customer journals/

electronic news letters”

* = in response to the question: “Which of these criteria differentiates your online offerings from that of your competitors?”

** = in response to the question: “Which of these instruments do you use for customer retention via the WWW?”

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3.3. Descriptive Analysis

Same as in the original survey, the remaining cases constitute a heterogeneous selection of companies from all industry backgrounds, company sizes, and business models, even if the original claim to be a representative selection for the German-speaking market must be relaxed. The vast majority (95.8%, corresponding to 450 cases) of companies are traditional “brick-and-mortar-”

enterprises, only 2.5% (6 cases) are spin-offs and only 0.5% (corresponding to two cases) are e-commerce start- ups.³ A number of 128 (or 27.3%) of the companies represented generate 50% or more of their revenue from selling services, i.e. can be viewed as belonging to the service industry. A number of 74 (or 15.8%) of the businesses generate 10% or more of their revenue online and can thus be regarded as “true E-Businesses” [1].

Moreover, 249 (or 53.1%) of this subset of companies offer their customers the opportunity to place orders online.

Finally, 224 companies (or 47.6%) specify consumers as their main customer segment, 215 companies (or 45.8%) stated that they mainly serve businesses. Another 18 (or 3.7%) mainly serve administrations, thus consider themselves as B2A-companies.³Therefore, the data set obtained from the survey can be considered appropriate for testing hypotheses about the general effectiveness of mastering the media characteristics of the WWW, and nearly ideal for performing a separate analysis of B2B- and B2C-enterprises.

3.4. Operationalization and Encoding of Variables

The indicator variables used in the survey in order to record the accommodation of specific media characteristic of the WWW on corporate web sites are displayed in Table 1. At the level of theoretical resolution that our research targets, indicator variables that test for different subdimensions or aspects of a web feature, yet all share a sufficient fraction of variance are difficult to derive, which is why we employed scores instead of complex constructs in several places.

3.5. Conceptualization and Operationalization of Corporate Success in E-Business

We limit our view on the concept of corporate success in electronic business to the shareholders’ perspective.

3 The fraction of companies missing from 100% did not specify any of these options.

The concept is implemented such as to accommodate for the major theories on competitive advantage, value creation and firm performance [1]. It is operationalized as a score value obtained from an unweighted addition of the values of 13 indicator variables. Each of them constitutes a metric variable on an equidistant interval (or Likert-like-) scale, ranging from “1” (representing strong dissent) to “5” (representing strong agreement).

Their wording is as follows:

1. “improved corporate image”

2. “increased market share”

3. “increased customer retention”

4. “reduced marketing costs”

5. “reduced sales costs”

6. “purchased more cheaply”

7. “developed new markets”

8. “increased revenues”

9. “offered new services”

10. “increased customer satisfaction”

11. “increased customer loyalty”

12. “increased overall corporate earnings”

13. “increased corporate value”

The indicator variables are preceded by the question:

“To what extent have the goals from this list actually been accomplished due to your internet activities?”

3.6. Statistical Analysis and Hypothesis Testing with Path Modeling

For testing the hypotheses in our research model, we employ the path analysis method. This method allows us to model correlations between independent constructs (in contrast to the multivariate regression method, e.g.), while we can employ constructs that are measured

“directly”, i.e. via a single indicator variable (as opposed to covariance structure models, e.g., for which complex constructs should be employed).

The path models for testing the hypotheses are displayed in Figure 1, Figure 2, and Figure 3. In a first step, we perform an overall analysis employing the data from all 469 companies, leading to the results as shown in Figure 1. In a subsequent step, we resimulate the model, using a subset of the data of just the 215 B2B- companies and, in a separate analysis, of the 224 B2C- companies, cf. Figure 2 and Figure 3, respectively. Based on the sample correlations, variances, and number of cases for each variable from the data set, the model coefficients are estimated using the unweighted least squares (ULS-) method. Significance values have been obtained from repeated bootstrap analyses (1000 samples).

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global measures of fit:

GFI : 1.000 AGFI : 1.000 NFI : .998 RMR : .019

zeta success

.22 .24

.37

.29 .25

.34

.26

.32 .20

.28 interactivity

availability

ease-of-use information richness media richness and variety

individualization and customization

immediacy

connectivity

corporate success in E-Business

.25***

.27***

.11 .00 -.03 .07 .06 -.08

key for significance measures:

* : α< 0.10

** : α< 0.05

*** : α< 0.01 .21

GFI : 1.000 AGFI : 1.000 NFI : .999 RMR : .034

interactivity

availability

immediacy

connectivity

.42 .36

.32

.37

.25 .29

.26

.22 .23

.21

`

.15***

.19***

.08 .14***

.09*

.22***

.12**

-.07

* : α< 0.10

** : α< 0.05

*** : α< 0.01

Figure 1. Path Diagram and Results of the Estimation for all 469 general companies

Figure 2. Path Diagram and Results of the Estimation for 215 B2B-companies only

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.30

GFI : 1.000 AGFI : 1.000 NFI : .999 RMR : .039

.53 .42

.26

.43

.22

.32 .22

.21 .27 .28

.34

interactivity

availability

immediacy

connectivity

-.03 .12 .00 .22***

.18**

.20***

.08 .19***

* : α< 0.10

** : α< 0.05

*** : α< 0.01

Figure 3. Path Diagram and Results of the Estimation for the 224 B2C-companies only As we cannot assume that the influential constructs in

our model are per se uncorrelated, we have to include the correlations between them in our model. On the other hand, we are striving for a possibly parsimonious model and there is no reason to suppose that all influential constructs are highly correlated, either. Chin suggests that standardized path coefficients in a structural equation model, even if significant, should be above 0.2 in order to be considered meaningful, because otherwise, the amount of variance a path coefficient explains in the (inter-)dependent construct would be too low [8]. For this reason, before analyzing each model, we check the sample correlations and include a correlation between the influential constructs into our model only if it is above 0.2 and significant. (Thus, all correlations displayed in Figures 1, 2, and 3 are highly significant at the 1%- level.) – Consequently, we will apply the same criterion when determining the relevant influential constructs from the results of our numerical analysis.

3.7. Measures for Global Model Fit

The measures for global model fit included in Figure 1, Figure 2, and Figure 3 mostly suggest that our covariance structure model fits the underlying data quite well. The values for GFI, AGFI and NFI clearly exceed

the recommended minimum value of 0.9 for all analyses, and the RMR value remains well below the commonly desired maximum of 0.1 [11]. Therefore, we conclude that our numerical models exhibit sufficient overall fit and that none need be rejected. Note that, as the ULS method was used for estimating the model coefficients and as the values of several indicator variables are not distributed normally, certain indices and quality measures for global model fit (e.g. CFI or the chi-square value) are not applicable and have therefore not been calculated.

3.8. Results of the Statistical Analysis

The numerical results for our research model can be obtained directly from the path coefficients and significance measures in the path models displayed in Figure 1, Figure 2, and Figure 3.

In the general case, there are path coefficients from five of the web characteristics to the dependent construct that exhibit a sufficient level of significance.

(Considering the large sample size of more than 200 cases employed in each analysis, a significance level of a mere 10% cannot be considered sufficient. Therefore, we demand that a path coefficient be at least significant at the 5%-level in order to be considered significant.)

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Strong statistical support (i.e. path coefficient significant at 1%-level) is found for the assumption that interactivity, immediacy, media richness and –variety, and information richness of companies’ websites positively impact corporate success. We obtain support (i.e. path coefficient significant at 5%-level) for the hypothesized influence of ease-of-use on corporate success. However, applying Chin’s proposition about the relevance of paths and relaxing it somewhat, we find that only information richness and, to a lesser extent, immediacy can be considered substantial success factors for web sites in the case of general companies.

For B2B-companies, only the two web features interactivity and immediacy can be backed from our numerical analysis. However, their path coefficients are each well above 0.2 and highly significant at the same time, suggesting that these two web features are relevant success factors for web sites in the case of B2B- companies.

In the case of B2C-companies, our model leads to the identification of four success factors among the web features: There is strong statistical support for the assumed positive impact of media richness and –variety, availability and ease-of-use on corporate success.

Further, support is found for the hypothesized impact of information richness on corporate success .

Finally, in our covariance structure model 21% of the variance of the endogenous construct representing corporate success are explained by the exogenous constructs in the general case, 16% in the numerical analysis of B2B-companies only, and 30% in the analysis concentrating on the B2C-companies.

4. Discussion

4.1 Summary and Explanation of the Findings Comparing the statistical analysis for the case of general companies with the two separate analyses for each customer segment, it is apparent that the web features which significantly load on corporate success in the former can be interpreted as the combination of the web features found to have significant impact on corporate success in the latter two analyses. The web features that are drivers for corporate success are disjoint in the specific case of each B2B- or B2C-companies only.

Therefore, in the general model their level of significance has been mitigated and their path coefficient has decreased. The construct for information richness seems to be just the opposite of this rule: Its path coefficient and level of significance is the highest in the

general model. This is probably due to our approach to only include the relevant correlations in the model.

In summary, a general corporate web site is found to be a success driver if it is rich in information (content) and updated frequently. These two factors are the key factors for attracting and retaining surfers, maintaining a continuous level of visits to the site, thus making it an effective interface for communication and business.

These two factors are supported if the web site also offers interactive features, content from a variety of media and if it is easy to use.

In the case of companies with business customers, interactivity of the web site and frequent updates are the only two, but very strong success factors among the web features. Web users who surf for professional reasons typically view a web site as an interface for engaging in business activities. They expect to find information about products and services which is up-to-date and they want to interact with the company “behind” the site, as they need service and consulting. In practice, these B2B- customers are referred to as “buyers, not browsers”.

While other features may be nice to have as “add-ons”

they are not critical and of less importance for business users when they make purchase decisions, e.g.

On the contrary, consumers typically expect from a corporate web site that it integrates content from different types of media. For consumers, the benefit from accessing a web site consists in that it readily offers a variety of preselected and aggregated content and functionalities all in one interface such that they do not have to research and filter the content themselves. Plus, a web site must be easy and convenient to use. These are the reasons explaining the success of some portal sites such as yahoo.com. Additionally, as the internet connection is a real bottleneck for most consumers since they do not have high-speed internet access (which was especially true in the German-speaking market at the time of the survey) it is essential that web sites are designed for short download times and kept available (even in situations where the web is slow due to a high traffic load, e.g. in the evening hours).

4.2. Interpretation of Selected Results

As for the evaluation of those web features who do not prove to be significant in one or all of the three numerical analyses, there are generally two possible interpretations:

1. Individually, the respective web feature may have a positive impact on corporate success, but as most of the companies in the segment-specific- or in the general context have already successfully

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implemented this web feature, no discriminatory power can be attributed to this feature anymore.

2. The respective web feature might be a “must have”, i.e. a prerequisite which constitutes no particular benefit if present, but whose absence can be a strong inhibitor for web-based transactions, such as trust is ([5], p. 192).

4.3. Limitations and Weaknesses of the Research The core shortcoming of our research is that we did not implement the web features with complex constructs, but with single indicator variables. Although the theoretical breadth of most concepts representing a web feature is certainly very limited, we might have recorded some concepts with higher resolution, reliability, and validity by operationalizing it as several indicator variables.

Lag problems might be another shortcoming of our research. As it takes time for the users to get used to certain features and functionalities on a corporate web site, it also takes time for the positive effect on corporate success to become visible or measurable, whereas the negative impact caused by the necessary investment occurs immediately. Further, as the E-Business history or track record of most companies is still relatively young, on the average it was about 2 years at the time of the survey, the above findings should be interpreted as reflecting an early stage of development.

The high fraction of explained variance of the construct for “corporate success in E-Business” in our model must also be reviewed critically. It is important to note that it is not exclusively accounted for by the exogenous constructs in our model, but that (parts of) the same variance can also be explained by other influential factors, i.e. competing constructs or constructs causing the use of certain web features, e.g. the implementation of an Internet strategy that makes it necessary to include certain features into a corporate web page. We did not control for these factors, since our model focuses on the effectiveness of web features as a singular theoretical concept.

5. Conclusion

5.1 Suggestions for further Research

Weighing the contributions of our study against its limitations and shortcomings, it is clear that our contribution must be viewed as a first-level analysis – as a “snapshot” and not as final empirical evidence. It

leaves a number of issues open for future empirical research. Some suggestions are:

• The survey should be repeated in a similar manner in order to assess how the identified interrelations change with time – especially as E- Business slowly matures – and vary in different markets.

• In future surveys, the web features should possibly all be implemented as multi-item measures. Then, more advanced numerical techniques, such as covariance structure modeling, could be employed.

• Finally, as web features investigated in this paper merely represent the media characteristics of corporate web sites viewed as a customer interface. Therefore, in future research, the effectiveness of web features should be researched in combination with, e.g., strategies for online selling, Internet marketing or integrated E- Business concepts such as ECCRM, one-to-one- marketing or mass customization.

5.2 Managerial Implications

The results of our research provide practical help for IT-managers and executives on how to direct and prioritize their investment decisions with respect to the features and functionalities of their corporate WWW- presence, although, naturally, certain minimum levels must be fulfilled for most features simply in order to ensure site operation and minimum user acceptance.

Based on the main customer segment that a company targets with its web site, decision makers should channel their investments as follows:

1. If the company operates a web site directed at a general user audience, executives should, as a first step, ensure information- and functionality richness of the site and that it be kept up-to-date.

As a second step, they might consider implementing interactive features, i.e. a “feedback channel” for their customers. Further, they should consider cooperating with other companies in order to be able to offer various content on their web site.

2. If the company operates a B2B-website, the message to its executive in charge is simple and clear: They should keep the web site up-to-date and ensure that it is a transparent interface making it easy for their customers to communicate with their company.

3. If the web site is directed at consumers, it should offer a variety of preselected and aggregated content and, to a lesser extent, functionalities all in one interface. Plus, navigating the site must be

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kept simple and intuitive. Also, as most consumers still do not have broadband internet access, the site design should accommodate for slow internet connections, and executives should ensure that the site remains accessible even during traffic peaks.

6. Acknowledgements

We would like to thank the anonymous HICSS reviewers and, especially, the associate editor for their constructive and helpful comments, which have greatly helped to improve an earlier version of our manuscript.

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