assessing the of potential of e-business models: towards a framework for assisting decision-makers

European Journal of Operational Research 160 (2005) 365–379

www.elsevier.com/locate/dsw

Assessing the of potential of e-business models:towards a framework for assisting decision-makers

Jeremy Hayes *, Pat Finnegan

University College Cork, Cork, Ireland

Received 1 October 2002; accepted 24 July 2003

Available online 6 December 2003

Abstract

Decision makers are faced with an enormous range of electronic business models from which to choose. The process

of fully researching each of these models can prove daunting. Such research is a feature of what has been termed the

‘‘intelligence phase’’ of decision making. This phase is important as options excluded at this stage do not get considered

at a later stage. This paper develops a prerequisites framework for use at the intelligence phase to exclude models that

are incompatible with prevailing organisational and supply chain characteristics. The framework assesses the following

characteristics: economic control, supply chain integration, functional integration, innovation and input sourcing. The

paper utilises a series of five point Likert scales to operationalise these characteristics so that they can be used by

decision makers to efficiently manage ‘‘intelligence phase’’ activities.

� 2003 Elsevier B.V. All rights reserved.

Keywords: Electronic business models; Decision support framework; Intelligence tool; Organisational characteristics; Supply-chain

characteristics

1. Introduction

The electronic business landscape is confusingfor many new entrants, and many of them face the

paradox that hesitation would run the risk of

being left behind, but rushing in and making an

incorrect choice regarding electronic business ini-

tiatives could have dire consequences for organi-

sations (Wise and Morrison, 2000). Internet-only

or ‘‘dot.com’’ models have proven particularly

* Corresponding author.

E-mail addresses: [email protected] (J. Hayes), p.finnegan@

ucc.ie (P. Finnegan).

0377-2217/$ - see front matter � 2003 Elsevier B.V. All rights reserv

doi:10.1016/j.ejor.2003.07.013

vulnerable. For example, the ‘‘dot.com’’ implosion

of Spring 2000 led to a large number of high-

profile dot.com collapses including boo, ClickM-ango and eToys (Howcroft, 2001). ‘‘Clicks and

Mortar’’ strategies have also met with mixed suc-

cess e.g. Wall Street Journal Interactive (Chen,

2001) and Fyffes� World-of-Fruit. Electronic

business poses significant challenges for organisa-

tions as it affects both how organisations relate to

external parties (customers, suppliers, partners,

competitors, and markets) – and how they operateinternally in managing activities, processes, and

systems (Rayport and Jaworski, 2001). Porter

(2001) argues that the companies that succeed with

e-business will be those that use the Internet in

ed.

mail to: [email protected]

366 J. Hayes, P. Finnegan / European Journal of Operational Research 160 (2005) 365–379

conjunction with their traditional business modelsand activities. Ben Lagha et al. (2001) argue that

‘‘after an initial phase of euphoria and the fol-

lowing disillusionment’’ in relation to electronic

business, it is important to understand how busi-

ness models function. Linder and Cantrell (2000)

argue that existing frameworks are not sufficient to

describe the rich array of business model choices

facing managers in electronic business environ-ments. In particular, decision makers have a diffi-

cult task in assessing the range of proposed models

in order to determine those that are most suitable.

The objective of this paper is to develop a

‘‘prerequisites framework’’ for assisting decision

makers assess the suitability of electronic business

models during the intelligence phase of the deci-

sion making process. Following this introduction,the business model concept is discussed and the

range of models proposed by researchers explored.

The issue of deciding on appropriate business

models is then outlined. This is followed by an

explanation of the theoretical grounding for the

proposed framework, and a discussion of its op-

erationalisation as a series of Likert scales. The

paper concludes with thoughts on refining andtesting the framework.

Table 1

Aspects of an e-business model (Osterwalder and Pigneur, 2002)

Product innovation • Target customer segment

• Value proposition

• Capabilities

Customer relationship • Information strategy

• Feel and serve

• Trust and loyalty

Infrastructure management • Resources

• Activity configuration

• Partner network

Financials • Revenue model

• Cost structure

• Profit/loss

2. Models of electronic business

Business models are possibly the most dis-

cussed, yet least understood area of electronic

business (Alt and Zimmerman, 2001; Rappa,2003). Osterwalder et al. (2002) make the point

that consultants, executives, researchers and jour-

nalists have ‘‘abusively’’ used the phrase ‘‘business

model’’ but have ‘‘rarely given a precise definition

of what they exactly meant by using it’’, and that

this has led to the a loss of credibility of the con-

cept.

Mahadevan (2000) argues that a business modelis a blend of three streams; value, revenue, and

logistics. The value stream is concerned with the

value proposition for buyers, sellers and market

makers. The revenue stream identifies how the

organisations will earn revenue, and the logistics

stream involves detailing how supply chain issues

will affect the organisations involved.

Timmers (1999) defines a business model as ‘‘anarchitecture for product, service and information

flows’’, incorporating a description of the sources

of revenue, the actors involved, their roles, and the

benefits to them. According to Timmers (1999), an

electronic business model is comprised of compo-

nents, linkages and dynamics. Components are

factors such as customer scope, product/service

scope, customer value, pricing, revenue sources,connected activities, implementation, capabilities

of the firm, and sustainability. Linkages exist when

one activity affects another in terms of cost-effec-

tiveness, and trade-offs and optimisation are

sought to find the right blend to achieve compet-

itive advantage. The dynamics represent how a

firm reacts to or initiates change to attain a new

competitive advantage, or to sustain an existingone, to have sustainable competitive advantage

and to perform better than its rivals in the long

term (Afuah and Tucci, 2001).

Osterwalder and Pigneur (2002) take a more

meticulous approach to the discussion of e-busi-

ness models. They propose an e-business model

ontology, which they define as a ‘‘rigorous defini-

tion of the e-business issues and their interdepen-dencies in a company�s business model’’. The

e-business model ontology focuses on four aspects

of the organisation, product innovation, infra-

structure management, customer relationship and

financials as shown in Table 1.

Timmers (1999) has argued that architectures

for business models can be identified through the

J. Hayes, P. Finnegan / European Journal of Operational Research 160 (2005) 365–379 367

deconstruction and reconstruction of the valuechain. Value chain elements are identified as well

as the possible ways that information can be

integrated along the value chain and between the

respective value chains of the parties that are

interacting. Thus, it is possible to conceive a large

number of business models, although, as Timmers

(1999) argues, this does not mean that a given

model will be commercially viable.Rappa (2003) proposes eight categories of e-

business model and identifies 36 models. Ticoll

et al. (1998) propose four different types of models,

and Timmers (1999) identified 11 e-business

models for business-to-business (B2B) trading. It is

clear that there is overlap between models pro-

posed by different researchers. In an attempt to

reconcile some overlap Table 2 presents fourclassifications of models and arranges them in a

manner that illustrates commonality.

A click and mortar merchant (Rappa, 2003) or e-

shop (Timmers, 1999) is the web presence of a

company or shop. This may exist merely to pro-

mote the goods and services of the company, but it

can also be equipped with on-line purchasing,

ordering and payment facilities. A virtual merchant

(Rappa, 2003) is a web-only ‘‘e-tailer’’, and a

Table 2

A synthesis of proposed business models

Timmers (1999) Ticoll et al. (1998) Kap

E-shop

E-mall

E-procurement Cata

yield

Third-party marketplace Aggregation For

aggr

E-auction Agora/open market

Virtual community Alliance

Collaboration platform

Value-chain service provider

Value chain

Value-chain integration

Information brokerage

Trust services

catalog merchant is the web presence of an existingmail-order business.

An e-mall (Timmers, 1999) or a virtual mall

(Rappa, 2003) is a collection of e-shops. These are

usually grouped together under a well-known

brand, and often offer a common payment method.

Rappa (2003) sees virtual malls as evolving

towards metamediaries that will provide transac-

tion services such as financial settlement, qualityassurance, order-tracking, and billing services.

E-procurement is electronic tendering and pro-

curement on the Web (Timmers, 1999). Rappa

(2003) discusses e-procurement in terms of a

distributor model that connects large volume buy-

ers with product manufacturers, and the manufac-

turer model that bypasses intermediaries in order

to ‘‘compress the distribution channel’’. E-hubs(Kaplan and Sawhney, 2000) add value by in-

creasing the efficiency of the procurement process.

Maintenance, repair and operations (MRO) hubs

are horizontal markets that enable systematic

sourcing of operating inputs. These tend to be low

value goods with high processing costs. Yield man-

agers create spot markets for operating resources

like manufacturing capacity, labour and advertis-ing, allowing operations to expand or contract their

lan and Sawhney (2000) Rappa (2003)

Click and mortar merchant

model, virtual merchant,

catalogue merchant

Virtual mall, metamediary

logue hubs, MRO hubs,

managers, exchanges

Distributor, manufacturer

model

ward aggregator, reverse

egator

Buy/sell fulfilment, market

exchange, bounty broker

Auction broker, reverse auction

Vertical web community,

specialised portal/vortal,

Knowledge networks, open

source model

Content services

Trust services, transaction

broker


operations at short notice (Kaplan and Sawhney,2000). Online exchanges allow procurement man-

agers to deal with situations of high and low de-

mand by rapidly exchanging commodities needed

for production. The exchange maintains relation-

ships between buyers and sellers, without them

having to negotiate contracts or relationship terms

(Kaplan and Sawhney, 2000). Catalog hubs auto-

mate the systematic sourcing of non-commoditymanufacturing inputs. Catalog hubs are industry

specific and add value by lowering transaction costs

(Kaplan and Sawhney, 2000).

A third-party marketplace gives online access to

the product catalogue of a company (Timmers,

1999). Other features, such as, ordering, delivery,

and secure payments can be added by the third

party marketplace provider (Timmers, 1999) ormarket exchange (Rappa, 2003). In the aggregation

model proposed by Ticoll et al. (1998), one orga-

nisation directs the flow of transactions and places

itself in the role of intermediary, for example, a

large retailer or wholesaler. Rappa (2003) also

discusses marketplace/aggregation models in terms

of models such as buy/sell fulfilment (where cus-

tomers specify prices for orders including deliveryand a broker charges a fee for fulfilment) and

bounty brokers (where a broker provides a search

for a product/service and charges a fee).

The agora is a traditional marketplace where

buyers and sellers negotiate prices, and there is

little or no control over the transactions (Ticoll

et al., 1998). This corresponds to the e-auction

model proposed by Timmers (1999). An auction

broker (Rappa, 2003) is a site that conducts auc-

tions for sellers, and earns revenues from fees and

commission. In the case of the reverse auction

(Rappa, 2003), the buyer makes a bid for an item,

and the auction broker seeks fulfilment.

Virtual communities encourage interaction be-

tween members, and this can bring about longer

lasting relationships with customers. Vertical B2Bcommunities or specialised vortals (Rappa, 2003)

act as a comprehensive source of information for

particular markets, containing product informa-

tion, supplier and product directories and industry

news. Rappa (2003) also discusses the community

model in terms of knowledge networks which are

sites providing forums and expert advice on vari-

ous topics. The open source model is cited byRappa (2003) as an example of a community, and

revenue can be earned through selling services such

as product support and tutorials. The alliance

model (Ticoll et al., 1998) attempts to deliver high

integration of value, without a clear leader or

hierarchy.

Value-chain service providers (Timmers, 1999)

focus on a specific step of the value chain, such aslogistics or secure electronic payments, and earn

revenues from commission and transaction fees.

Collaboration platforms provide a set of tools

and an information environment so that teams can

co-operate on projects, regardless of location.

Virtual teams, such as engineers or consultants,

can use the platform for project support. Revenues

are earned from membership/service fees, andfrom selling the specialist tools (Timmers, 1999).

The value chain model (Ticoll et al., 1998) in-

volves a small number of organisations, or indeed

a single organisation that sources components

from a large number of suppliers, to provide a

highly customised product.

Value-chain integrators focus on integrating the

steps of the value chain, in order to add value.They earn revenues from consultancy and trans-

action fees (Timmers, 1999).

As a result of the vast amount of data available

on the Internet, specialist information brokers

(Timmers, 1999) or content services (Rappa, 2003)

have emerged to provide services such as infor-

mation search, customer profiling, investment

advice, and commercial business information ser-vices.

Trust services, are provided by certification

authorities and electronic notaries. These services

may charge subscription fees or service fees,

combined with software sales and consultancy

(Timmers, 1999; Rappa, 2003). A transaction bro-

ker (Rappa, 2003) is a third-party that provides

facilities to settle transactions.

3. Deciding on electronic business models

Dynamic environments can cause huge unpre-

dictability in the operation of an organisation

(Mintzberg, 1979). When an organisation is faced


with an uncertain supply chain, rapidly changingtechnology, repeated product evolution, or high

internal growth, the organisation is unable to

predict future conditions and cannot use stan-

dardisation mechanisms to co-ordinate the activi-

ties of the organisation (Mintzberg, 1979). There

seems to be much anxiety about the appropriate-

ness of various models for particular organisations

and industrial sectors (Wise and Morrison, 2000).Timmers (1999) argues that lack of understanding

of appropriate business models as well as concerns

about costs, security, legal issues and technology

are the reasons why many companies are hesitant

with electronic business. Heidegger (1977) pro-

poses that organisations should plan for the future

by creating a domain into which new business

processes, methods and models can be introduced.Such pre-emptive decision making has been con-

trasted with conventional decision making by

Rosenhead et al. (1986), who highlight the need for

more robust decision making processes in order to

develop pre-emptive manouveres. These type of

decision making processes are of particular interest

in relation to e-business models as organisations

lack sufficient knowledge of the area.Decision-making is regarded as one of the most

important activities carried out by an organisation

(Simon, 1960). Simon (1960) describes decisions as

being programmed and non-programmed. Deci-

sions that are programmed have defined proce-

dures associated with them, and that are repetitive.

Non-programmed decisions arise when a new

problem is encountered, and there is no specificprocedure mapped out to deal with it. The nature

of the problem may be extremely complex,

important enough to merit a customised solution,

or may be unique from the point of view that no

similar problem has arisen previously. The deci-

sion to engage in electronic business can be seen as

non-programmed as described by Simon (1960) as

new strategies and new technologies are required.In particular non-programmed decisions arise

when a new problem is encountered, and there is

no specific procedure mapped out to deal with it.

The nature of the problem may be extremely

complex, important enough to merit a customised

solution, or may be unique from the point of view

that no similar problem has arisen previously. The

classification of decisions regarding electronicbusiness models as non-programmed is in spite of

the wealth of frameworks for describing the effects

of electronic business on organisations (e.g. Porter,

2001) as few organisations have used such frame-

works.

Simon (1960) identifies four phases of decision-

making, the intelligence activity, the design activ-

ity, the choice activity and the review activities.The intelligence activity represents an examination

of the milieu for situations in which a decision is

required. The second phase involves devising,

forming and examining possible strategies. The

third phase involves choosing a particular strategy

or course of action. The final phase, the reviewing

phase, comprises all activities that appraise past

choices. In general, the intelligence activity hap-pens before the design activity, which in turn

happens before the choice activity.

A key aspect of the decision making process is,

therefore, the intelligence phase (Simon, 1960).

Sammon and Adam (2000) cite the work of Po-

merol (1994) in explaining that the intelligence

phase is crucial as alternatives not considered at

this stage are very unlikely to be involved in thedecision scenario at a later stage. They claim that

this is particularly important in designing more

robust decisions as it increases the number of

possible outcomes. Nevertheless, little research has

been conducted on determining appropriate e-

business models and strategies despite its impor-

tance to organisational decision-makers.

Finnegan and Sammon (2000) have attemptedto help decision makers with the intelligence phase

in relation to assessing the suitability of particular

organisations for data warehousing. Their re-

search identified a set of organisational factors, or

organisational prerequisites, that are critical for the

successful implementation of data-warehousing

projects. They define organisational prerequisites

as ‘‘necessary elements existing within the organi-sation which are examinable (internally) by the

implementing organisation, prior to the organisa-

tion undertaking the initiation of a data ware-

housing project’’ (Finnegan and Sammon, 2000).

Decision makers can use this list of organisational

prerequisites to help focus decision making activity

at the intelligence phase.


Coltman et al. (2001) highlight the importanceof the organisational environment to decisions

regarding electronic business. Environmental

characteristics are also seen as important by other

researchers. Drawing on the work of Van Over

and Kavan (1993), Finnegan and Golden (1995)

propose that the adoption of electronic business

applications is based on two factors; market

receptivity and the probability of competitoradoption. As shown in Fig. 1, the decision to en-

gage in electronic business is influenced by the cell

that corresponds to the organisation�s situation.

In Cell 1, organisations must establish electronic

business applications in order to survive as com-

petitors are likely to seize the opportunity. In Cell

2, there are significant competitive opportunities

by implementing electronic business ahead ofothers. In Cell 3, organisations have to decide

whether to chose electronic business in light of a

probable introduction by a competitor in a market

that is likely to resist electronic business activity.

Situations such as Cell 4 offer organisations an

opportunity to pioneer advances in electronic

business, but must face the possibility that such

moves will be rejected by the market (Finneganand Golden, 1995).

Frameworks such as this one can aid managers

at a high level with the decision to implement

electronic business applications. However, they are

not specific enough to help with the lower level

detail required in relation to making decisions

about models of electronic business.

This paper aims to utilise the prerequisitesconcept to develop a framework to help managers

Cell 1 Cell 3

Cell 2 Cell 4

Probable

Improbable

Immediate

Competitor

Adoption

More Less

Market

Receptivity

Fig. 1. Potential electronic commerce opportunities (from

Finnegan and Golden, 1995).

focus intelligence phase activities by excludingfrom the decision making process electronic busi-

ness models that are not compatible with prevail-

ing organisational and environmental/supply chain

conditions.

4. Towards a prerequisites model

In order to construct the prerequisites model,

we have examined classification theories of par-

ticular electronic business models based on the

work of Ticoll et al. (1998), Timmers (1999), and

Kaplan and Sawhney (2000). It is clear from such

research that particular models are more likely to

operate under certain conditions.

As shown in Fig. 2, Ticoll et al. (1998) identifyfour types of electronic business models based on

the degree of value integration (low to high) and

control (self-organising to hierarchical) in the

supply chain. These parameters are used to de-

scribe four characteristics of e-business commu-

nity, the open market, aggregation, value chain

and alliance as discussed in a previous section.

Ticoll et al. (1998) define economic control as thedegree to which a market has a leader that directs

the nature and flow of transactions, that is, the

degree to which it is hierarchical or self-organising.

Value is defined by Ticoll et al. (1998) as the

benefit that a user derives from using goods and

services. High value integration occurs when con-

tributions from many suppliers are bundled to-

gether to provide a unique product offering. Lowvalue integration occurs when the product offering

Fig. 2. E-business communities differentiate on axes of eco-

nomic control and value integration (Ticoll et al., 1998).


consists of an assorted basket of disparate goodsand services, for example, a supermarket.

Timmers� (1999) models were also discussed in a

previous section. Timmers classifies the eleven

models in relation to two characteristics; innova-

tion and functional integration as shown in Fig. 3.

The innovation dimension is concerned with the

degree to which the Internet allows a business to

perform processes that were not previously possi-ble, while functional integration refers to the de-

gree to which multiple functions are integrated

together within particular business models (Tim-

mers, 1999).

As shown in Fig. 4, Kaplan and Sawhney

(2000) classify business-to-business (B2B) ‘‘hubs’’

into four categories, by classifying manufacturing

inputs and operating inputs, by two types ofsourcing, systematic and spot. Manufacturing

Multiple

functions/

integrated

Single

function

Lower

Fun

ctio

nal I

nteg

rati

on

E-shop

E-procurement

Trust S

E-auction

E-mallValue

V

Degree of I

Fig. 3. Classification of internet bus

Wh

How Businesses Buy Operating Inputs

Systematic Sourcing MRO Hubs

Spot Sourcing Yield Managers

Fig. 4. The B2B matrix (Kapl

inputs are usually purchased from vertical suppli-ers and require specialised logistics and fulfilment

mechanisms. Operating inputs are purchased from

horizontal suppliers and involves negotiated con-

tracts with long term suppliers.

The classifications by Ticoll et al. (1998), Tim-

mers (1999) and Kaplan and Sawhney (2000) re-

late only to those models proposed by the

researchers in question. However, it is clear fromthe synthesis of electronic business models pre-

sented in Table 2 that there are similarities be-

tween models proposed by various researchers.

Therefore, by combining the work of Ticoll et al.

(1998), Timmers (1999), and Kaplan and Sawhney

(2000) with the synthesis that we developed in

Table 2, we propose that electronic business

models can be classified according to how theyexhibit varying degrees of economic control, value

Higher

Information Brokerage

ervices

Chain Service Provider

irtual Business Community

Collaboration Platform

Third-party marketplace

Value-chain Integrator

nnovation

iness models (Timmers, 1999).

at Businesses Buy

Manufacturing Inputs

Catalog Hubs

Exchanges

an and Sawhney, 2000).


chain integration, functional integration, businessinnovation and technical innovation. The low,

medium and high ratings as presented in Table 3

have been derived from the axes in Figs. 2–4, and

the relevant discussions about these models by the

researchers in question. The basis of our prereq-

uisites model is shown in Table 3.

Table 3 hypothesises that a particular business

model is more likely to succeed in a particularindustrial sector when the characteristics of the

sector match the conditions required for the

model. Table 3 shows a subset of the models dis-

cussed earlier in the paper as it takes into consid-

eration the synthesis in Table 2. This subset is

largely based on Timmers� work as it is represents

the broadest classification of models. It is also

worth considering that Timmers (1999) proposesan almost infinite number of business models

based on the interaction between value chains.

Consequently, it is possible to conceive a large

number of business models that are not discussed

in this paper. However, we have focused on busi-

ness models that have been verified by existing

research and did not seek to identify new models.

The characteristics (e.g. economic control,functional integration, etc.) referred to in Table 3

are those that have been identified in the frame-

works discussed above. These are reasonably

stable organisational and supply chain character-

istics, and are thus unlikely to change in the

medium term. It is clear that other factors will also

influence the viability of particular electronic

Table 3

Characteristics of business models

Business model Economic

control

Functional

integration

E-shop Low Low

E-mall Low Medium

E-procurement Medium Medium

E-auction Low Medium

Information brokerage Low Low

Trust services Low Low

Third party marketplace High High

E-hubs High High

Virtual communities Medium Medium

Value-chain integrators High High

Value-chain service providers Low Medium

Collaboration platforms Low High

business models. For example, financial aspectssuch as revenue streams have been identified by

a large number of researchers (Timmers, 1999;

Linder and Cantrell, 2000; Mahadevan, 2000;

Osterwalder et al., 2002) as an integral part of the

business model of an organisation. Our prerequi-

sites model does consider such factors, as they

should be thoroughly examined during the design

phase of decision making and or model is anintelligence phase tool.

5. Operationalising the model

The characteristics listed in Table 3 are very

high level. It was necessary to determine mea-

surements for each of the characteristics in orderto make the model suitable as a decision aid. This

was achieved by operationalising each character-

istic, and using five-point Likert scales to judge the

degree to which each operationalisation construct

relates to the organisation or market. This is

considered sufficient to be able to extract the low,

medium or high value to correspond to Table 3.

Decision-makers are asked to rate each relevantconstruct and to determine totals based on appli-

cable questions only.

5.1. Economic control

Economic control refers to the degree to which

a market is hierarchical or self-organising (Ticoll

Supply chain

integration

Innovation Sourcing

Low Low Systematic

Low Medium Systematic

Medium Medium Systematic

Medium Medium Spot/systematic

Low High Not applicable

Low Medium Not applicable

High High Spot/systematic

High High Spot/systematic

High High Not applicable


Medium High Not applicable



et al., 1998). To operationalise this characteristic,we examined the work of Porter (1985) in relation

to the five competitive forces. The scale is shown in

Table 4, and awards 1 where the statement is un-

true and five where the statement is completely

indicative of the situation.

5.2. Functional integration

Functional integration refers to the degree to

which multiple functions are integrated in a busi-

ness model (Timmers, 1999). In order to measure

the degree to which functions within an organisa-

tion are integrated, we developed a scale that

considers a detailed list of processes. Our list of

organisational processes was developed by adapt-

ing the modules of leading enterprise resource

Table 4

Measures of economic control

Economic control characteristic

The market for our products is largely managed/directed by regula

The market for our products is largely managed/directed by gover

The market for our products is largely directed by customers

Manufacturing outputs in our organisation are low in asset specifi

Switching costs for our customers are low

Proprietary product differences are quite high

Capital requirements for new entrants in this industry are not proh

Access to necessary inputs for new entrants in this industry is not

The proprietary learning curve for new entrants in this industry is

Access to distribution channels for new entrants in this industry is

Customers have a high propensity to substitute in this industry

Ability for customers to backward integrate is high

Buyers have huge bargaining leverage with our organisation

Price sensitivity for our products is high

The products of our organisation can be easily substituted

Our products do not have a high degree of brand identity

The market for our inputs is highly directed by suppliers

The market for our inputs is largely managed/directed by regulato

Manufacturing inputs in the organisation are high in asset specific

Switching costs for suppliers from our organisation are low

The market for our inputs is largely managed/directed by governm

The market for our products is highly directed by competitor beha

Manufacturing inputs of our organisation cannot be easily substitu

Suppliers place great importance on transaction volume

Suppliers are not very concentrated in our industry sector

Input costs have a large impact on total costs

Proprietary input differences are quite high

We have a low propensity to substitute suppliers

We do not have much bargaining leverage with suppliers

planning (ERP) systems. Table 5 shows the pro-cess elements categorised by function. Each ques-

tion asks the degree to which a particular process

is integrated with processes from other functions.

The scale uses a five point Likert scale where 0

represents no integration with other processes, and

4 represents a high level of integration.

5.3. Supply/value chain integration

Supply/value chain integration is considered to

be a measure of the degree to which the business

functions and processes of an organisation are

integrated with those of their supply chain part-

ners. Our list of organisational processes was

developed by adapting the modules of leading

enterprise resource planning (ERP) systems as we

Score

tory bodies 1 2 3 4 5

nment policy 1 2 3 4 5

1 2 3 4 5

city 1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

ibitive 1 2 3 4 5

prohibitive 1 2 3 4 5

not prohibitive 1 2 3 4 5

not prohibitive 1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

ry bodies 1 2 3 4 5

ity 1 2 3 4 5

1 2 3 4 5

ent policy 1 2 3 4 5

viour 1 2 3 4 5

ted 1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

Table 5

Measures of functional integration

Please indicate the extent to which the following are integrated with other processes and functions in the organisation Score

Customer and supplier address list/code processing with servicing, distribution, manufacturing, planning, and

financial functions

0 1 2 3 4

Purchase order processing with servicing, distribution, manufacturing, planning, and financial functions 0 1 2 3 4

Shipping scheduling with servicing, manufacturing, planning, and financial functions 0 1 2 3 4

Transportation management with servicing, manufacturing, planning, and financial functions 0 1 2 3 4

Tax reporting with servicing, distribution, manufacturing, planning, and financial functions 0 1 2 3 4

Negotiating customer credit terms with servicing, distribution, manufacturing, planning, and financial functions 0 1 2 3 4

Negotiating supplier credit terms with servicing, distribution, manufacturing, planning, and financial functions 0 1 2 3 4

Determining freight charges and terms with servicing, distribution, manufacturing, planning, and financial functions 0 1 2 3 4

Product line planning with servicing, distribution, manufacturing, and financial functions 0 1 2 3 4

Resource planning with servicing, distribution, manufacturing, and financial functions 0 1 2 3 4

Production forecasting with servicing, distribution, manufacturing, and financial functions 0 1 2 3 4

Material requirements planning with servicing, distribution, manufacturing, and financial functions 0 1 2 3 4

Master planning scheduling with servicing, distribution, manufacturing, and financial functions 0 1 2 3 4

Capacity requirements planning with servicing, distribution, manufacturing, and financial functions 0 1 2 3 4

Distribution requirements planning with manufacturing, planning, and financial functions 0 1 2 3 4

Inventory control with servicing, distribution, manufacturing, planning, and financial functions 0 1 2 3 4

Purchasing with servicing, distribution, manufacturing, planning, and financial functions 0 1 2 3 4

Supplier receipt scheduling with manufacturing, planning, and financial functions 0 1 2 3 4

Sales quotation management with servicing, distribution, manufacturing, planning, and financial functions 0 1 2 3 4

Sales order/invoice management with servicing, distribution, manufacturing, planning, and financial functions 0 1 2 3 4

Customer schedule planning with servicing, distribution, manufacturing, and financial functions 0 1 2 3 4

Configuring products with servicing, distribution, planning, and financial functions 0 1 2 3 4

Sales analysis with servicing, distribution, manufacturing, planning, and financial functions 0 1 2 3 4

General ledger with servicing, distribution, manufacturing, planning, and financial functions 0 1 2 3 4

Handling multiple currencies with servicing, distribution, manufacturing, planning, and financial functions 0 1 2 3 4

Accounts receivable with servicing, distribution, manufacturing, and planning functions 0 1 2 3 4

Cash management with servicing, distribution, manufacturing, and planning functions 0 1 2 3 4

Accounts payable with servicing, distribution, manufacturing and planning functions 0 1 2 3 4

Fixed asset management with servicing, distribution, manufacturing and planning functions 0 1 2 3 4

Payroll with servicing, distribution, manufacturing and planning functions 0 1 2 3 4

Cost management with servicing, distribution, manufacturing and planning functions 0 1 2 3 4

Defining manufacturing work centres with servicing, distribution planning, and financial functions 0 1 2 3 4

Managing batch processing of products with servicing, distribution, planning, and financial functions 0 1 2 3 4

Works order management with servicing, distribution, planning, and financial functions 0 1 2 3 4

Shop floor control with servicing, distribution, planning, and financial functions 0 1 2 3 4

Quality management with servicing, distribution, planning, and financial functions 0 1 2 3 4

Service contracts management with distribution, manufacturing, planning, and financial functions 0 1 2 3 4

Service contract billing with distribution, manufacturing, planning, and financial functions 0 1 2 3 4

Service call tracking with distribution, manufacturing, planning, and financial functions 0 1 2 3 4

Managing repairs/returns to suppliers with servicing, distribution, manufacturing, planning, and financial functions 0 1 2 3 4


have done for functional integration. Thus, theindividual items in the scale are the same as in

Table 5 (e.g. production forecasting, inventory

control) except that the questions refer to inte-

gration with processes in other organisations ra-

ther than internal functions. This is shown in

Table 6. A score of 0 represents no integration

with supply chain partners and 4 represents a high

level of integration.

5.4. Innovation

The degree of innovation of an e-business

model can be defined as the extent to which pro-

cesses can be performed via the internet that were

not previously possible. We have divided innova-

tion into internal and external components based

on the firm�s ability (internal) to innovate orassimilate innovations within the innovative envi-

Table 6

Measures of supply chain integration

Please indicate the extent to which the following are integrated with processes and functions in other

firms in the supply chain (i.e. customers and suppliers)

Score

Customer and supplier address list/code processing 0 1 2 3 4

Purchase order processing 0 1 2 3 4

Shipping scheduling 0 1 2 3 4

Transportation management 0 1 2 3 4

Tax reporting 0 1 2 3 4

Negotiating customer credit terms 0 1 2 3 4

Negotiating supplier credit terms 0 1 2 3 4

Determining freight charges and terms 0 1 2 3 4

Product line planning 0 1 2 3 4

Resource planning 0 1 2 3 4

Forecasting 0 1 2 3 4

Material requirements planning 0 1 2 3 4

Master planning scheduling 0 1 2 3 4

Capacity requirements planning 0 1 2 3 4

Distribution requirements planning 0 1 2 3 4

Inventory control 0 1 2 3 4

Purchasing 0 1 2 3 4

Supplier receipt scheduling 0 1 2 3 4

Sales quotation management 0 1 2 3 4

Sales order/invoice management 0 1 2 3 4

Customer schedule planning 0 1 2 3 4

Configuring products 0 1 2 3 4

Sales analysis 0 1 2 3 4

General ledger 0 1 2 3 4

Handling multiple currencies 0 1 2 3 4

Accounts receivable 0 1 2 3 4

Cash management 0 1 2 3 4

Accounts payable 0 1 2 3 4

Fixed asset management 0 1 2 3 4

Payroll 0 1 2 3 4

Cost management 0 1 2 3 4

Defining routings/operations/work centres in manufacturing 0 1 2 3 4

Managing batch processing of products 0 1 2 3 4

Works order management 0 1 2 3 4

Shop floor control 0 1 2 3 4

Streamlining labour and material planning 0 1 2 3 4

Quality management 0 1 2 3 4

Servicing contracts management 0 1 2 3 4

Servicing contract billing 0 1 2 3 4

Servicing call tracking 0 1 2 3 4

Managing repairs/returns to suppliers and from customers 0 1 2 3 4


ronment of the industrial sector (external). Moore

and Benbasat (1991) developed a list of items to

measure the technical (internal) innovation of a

firm based on characteristics such as relative

advantage, compatibility, image, ease of use, resultdemonstrability, visibility and trialability. These

items were reworded so as to refer to information

technology (Moore and Benbasat�s, 1991, scale

referred to the use of a PWS by employees in the

organisation). The adapted scale uses 25 Likert-

scale items from strongly disagree to strongly

agree as shown in Table 7.Gatignon and Robertson (1989) identified com-

petitive price intensity and industry concentration

Table 7

Measures of internal technical innovation

Innovation characteristic Score

Using IT enables me to accomplish tasks more quickly 1 2 3 4 5

Using IT improves the quality of work that I do 1 2 3 4 5

Using IT makes it easier to do my job 1 2 3 4 5

Using IT enhances my effectiveness on the job 1 2 3 4 5

Using IT gives me greater control over my work 1 2 3 4 5

Using IT increases my productivity 1 2 3 4 5

Using IT is compatible with all aspects of my work 1 2 3 4 5

Using IT fits well with the way I like to work 1 2 3 4 5

People in my organisation who work with IT have more prestige than those who do not 1 2 3 4 5

People in my organisation who work with IT have a high profile 1 2 3 4 5

Using IT is a status symbol in my organisation 1 2 3 4 5

My interaction with IT is clear and understandable 1 2 3 4 5

It is easy to get IT to do what I want it to do 1 2 3 4 5

Overall IT in this firm is easy to use 1 2 3 4 5

Learning to operate different IT applications is easy for me 1 2 3 4 5

I would have no difficulty telling others about the results of using IT 1 2 3 4 5

I could communicate to others the consequences of using IT 1 2 3 4 5

The results of using IT are apparent to me 1 2 3 4 5

I would have difficulty explaining why using IT may or may not be beneficial 1 2 3 4 5

In my organisation, one sees IT applications on many desks 1 2 3 4 5

IT is not very visible in my organisation 1 2 3 4 5

Before deciding whether to use IT applications, I was able to properly try them out 1 2 3 4 5

I was permitted to use IT on a trial basis long enough to see what it could do 1 2 3 4 5

I am able to experiment with the IT applications as necessary 1 2 3 4 5

I can have new IT applications for long enough periods to try them out 1 2 3 4 5


as significant determinants for the adoption of a

high technology innovation. Company centralisa-tion was also identified as being strongly related to

adoption. Only items that were found to be sta-

tistically significant were chosen when adapting

the scale. The scale for external innovation is

therefore based on the following subscales; indus-

try concentration, competitive price intensity, and

organisational centralisation. First, the issue of

industry concentration is considered by asking thedecision-maker to estimate the market share (in

Table 8

Measures of external innovation

Statement

In your organisation, employees are hired and trained to handle o

specific departments

How frequently does price-cutting take place in your industry

In your organisation, detailed written job descriptions for employe

Compared to other organisations in your industry sector, written p

guide the actions of employees are used

In your organisation employees are transferred across different dep

percentage) of the top three firms in the indus-

try. A five-point scale with intervals of 20% isused to determine low to high concentration.

Second, competitive price intensity is assessed by

asking the decision-maker how frequently price-

cutting takes place in the industry. A five-point

scale is used to determine this, where one is never

and 5 is very frequently. Third, the issue of or-

ganisational centralisation is assessed using the

scale shown in Table 8. Here the decision-makeris asked to use the same five-point scale as for

Score

nly specific tasks in 1 2 3 4 5

1 2 3 4 5

es are used 1 2 3 4 5

olicies and procedures that 1 2 3 4 5

artments 1 2 3 4 5


competitive price intensity in relation to each ofthe four questions.

5.5. Sourcing

Sourcing refers to the way in which inputs are

sourced by the organisation, either systematically

from a long-term supplier or through spot mar-

kets. The issue of sourcing raw materials is morestraightforward as manufacturing and operating

inputs are either sourced systematically or on spot

markets. Our instrument uses a five-point scale to

determine the percentage of operating and manu-

facturing inputs that are sourced in systematic and

spot markets. On this scale, 1 represents 0–20%, 2

represents 21–40%, 3 represents 41–60%, 4 repre-

sents 61–80% and 5 represents 81–100%. Based onthis scale, a profile of the company is constructed

based on the work of Kaplan and Sawhney (2000)

to indicate the relative importance of MRO hubs,

catalog hubs, yield managers and exchanges for

the company.

6. Using the prerequisites model

It is important to note that the prerequisites

model is intended for use in the intelligence phase

of decision making to narrow down a large num-

ber of possible business models so that a smaller

number of alternatives can be examined more

thoroughly. Thus the aim of the model is to ex-

clude those electronic business models that are notsuitable given prevailing organisational and supply

chain characteristics. For each scale the decision-

maker needs to determine the number of attributes

that are applicable to their organisation. For

example, an organisation that does not hold

inventory excludes the questions relating to this

Table 9

Summary data for example

Economic

control

Supply chain

integration

Functiona

integration

Score 80/150 82/144 85/156

Percentage 53% 56.9% 54.5%

Rating Medium Medium Medium

topic. Based on the number of questions to beanswered, the decision maker then determines the

maximum and minimum available scores for each

attribute; economic control, supply chain integra-

tion, functional integration, external innovation,

internal innovation and sourcing. For most of the

scales, the minimum score will be the same as the

number of questions, while the maximum score is

determined by multiplying the number of ques-tions by five. The exceptions are functional and

supply chain integration where the minimum score

will be zero and the maximum is calculated by

multiplying the number of questions answered by

four. The marks awarded for each attribute are

then classified as either low, medium or high based

on the minimum and maximum score. Low,

medium and high rating are determined as being0–34%, 35–69%, and 70–100% (respectively) of the

available marks. This classification is then com-

pared with the classification of business models

presented in Table 3 to determine the suitability

of each electronic business model for the organi-

sation.

To illustrate the scoring, the framework was

applied to a manufacturing company known to theauthors. The rating scales were completed by three

senior managers within the company. The calcu-

lated scores and associated rating for the company

are shown in Table 9. For example, the maximum

score for Functional Integration is calculated as

156 – being 39 times 4. This is because one ques-

tion out of the possible 40 items was considered

not applicable by respondents, so answers werereceived for 39 items. The sum of the scores for the

39 items was 85, resulting in a score of 54.5% (85

divided by 156).

The ratings in Table 9 were then compared with

the characteristics of e-business models previ-

ously shown in Table 3. An extract of the results is

l Internal

innovation

External

innovation

Sourcing

85/125 20/34 –

68% 59% –

Medium Medium Systematic

Table 10

Comparing the framework with company ratings

Business model Economic

control

Supply chain

integration

Functional

integration

Innovation Sourcing

Third party

marketplace

Medium

(high)

Medium

(high)

Medium

(high)

Medium–high

(high)

Systematic

(spot/systematic)

E-hubs Medium

(high)

Medium

(high)

Medium

(high)

Medium–high

(high)

Systematic

(spot/systematic)

Virtual communities Medium

(medium)

Medium

(high)

Medium

(medium)

Medium–high

(high)

Systematic

(not applicable)

Value-chain integrators Medium

(high)

Medium

(high)

Medium

(high)

Medium–high

(high)

Systematic

(not applicable)


shown in Table 10. The ratings in parenthesisare those required for individual e-business mod-

els from Table 3. The four models in the table

are unsuitable due to the differences between

framework scores and the model characteristics.

In this case, the unsuitability of the models is

evident across the vast majority of characteristics.

However, the borderline score of 68% for inno-

vation would need greater attention if this was theonly characteristics preventing suitability. It is

important to note that this example is for illus-

trative purposes only and does not serve to test the

model.

7. Conclusions

We believe that our framework has the poten-

tial to help decision makers by providing a method

of excluding from consideration those electronic

business models that are unsuitable given prevail-

ing organisational and environmental characteris-

tics. The framework is aimed at existing businesses

considering an e-business model rather than those

designing Internet-only organisations where the e-business model is also the business model. The

framework builds on theories of pre-adoption

prerequisites on order to assist with the intelligence

phase of decision-making. The authors propose

that the framework may increase an organisation�schances of choosing an appropriate e-business

model, by helping decision-makers identify a

shorter list of models for full assessment. Themodel as presented in this paper is conceptual, and

has not been empirically tested at this point. Our

findings from the pre-test of this model are posi-tive. However, further testing is required.

As a first step in refining the model, it will be

necessary to study decision makers within organi-

sations that have investigated electronic business

models from the perspective of potential adoption.

The study would need to determine the scores for

each organisation based on prevailing organisa-

tional and supply chain characteristics at the timethat the decision was made. It would then be

necessary to assess the degree to which models ex-

cluded by the prerequisites framework were ex-

cluded by the decision makers at some stage in the

decision making process. The most important as-

pect of the study would be to consider whether the

prerequisites model could have identified models

for exclusion at the intelligence phase based onincompatibility between e-business model require-

ments and the prevailing organisational and envi-

ronmental characteristics. By studying a number of

organisations in this manner it should be possible to

test the applicability of the model and refine it for

further use.

References

Afuah, A., Tucci, C., 2001. Internet Business Models and

Strategies. McGraw-Hill, New York.

Alt, R., Zimmerman, H-D., 2001. Preface––Introduction to

special section––business models. Electronic Markets 11 (1),

3–9.

Ben Lagha, S., Osterwalder, A., Pigneur, Y., 2001. Modeling

e-business with eBML. In: 5e Conf�erence International de

Management des R�eseaux d�Entreprises, Mahdia, October

25–26. Available at http://inforge.unil.ch/aosterwa/.

Chen, S., 2001. Strategic Management of e-Business. Wiley,

Chichester.

http://inforge.unil.ch/aosterwa/


Coltman, T., Devinney, T.M., Latukefu, A., Midgeley, D.F.,

2001. E-business: Revolution, evolution or hype? California

Management Review 44 (1), 57–85.

Finnegan, P., Golden, W., 1995. Electronic commerce: Imple-

mentation issues for Irish management. In: Walsh, J. (Ed.),

Proceedings of the Management Research in Ireland Con-

ference, Cork Ireland, September 12–13. pp. 1–10.

Finnegan, P., Sammon, D., 2000. The ten commandments of

data warehousing. DATABASE 31 (4), 82–91.

Gatignon, H., Robertson, T.S., 1989. Technology diffusion: An

empirical test of competitive effects. Journal of Marketing

53 (January), 35–49.

Heidegger, M., 1977. The Question Concerning Technology

and Other Essays. Harper Cohen.

Howcroft, D., 2001. After the goldrush: Deconstructing the

myths of the dot.com market. Journal of Information

Technology 16, 195–204.

Kaplan, S., Sawhney, M., 2000. E-hubs: The new B2B

marketplaces. Harvard Business Review (May–June), 97–

103.

Linder, J.C., Cantrell, S., 2000. Changing business models: Sur-

veying the landscape. Working Paper, Institute for Strategic

Change, Accenture. Available at http://www.accenture.com/

xdoc/en/ideas/isc/pdf/Surveying_the_Landscape_WP.pdf-

whitepaper.

Mahadevan, B., 2000. Business models for internet based e-

commerce: An anatomy. California Management Review 42

(4), 55–69.

Mintzberg, H., 1979. The Structuring of Organisations.

Prentice Hall, Englewood Cliff, NJ.

Moore, G.C., Benbasat, I., 1991. Development of an instru-

ment to measure the perceptions of adopting an information

technology innovation. Information Systems Research 2 (3),

192–222.

Osterwalder, A, Pigneur, Y., 2002. An e-business model

ontology for modelling e-business. In: Bled Electronic

Commerce Conference, Bled, June 17–19. Available at

http://inforge.unil.ch/aosterwa.

Osterwalder, A., Ben Lagha, S., Pigneur, Y., 2002. An ontology

for developing e-business models. In: Proceedings of IFIP

DSIAge�2002, Cork, July 3–7, pp. 709–725.

Pomerol, J.C., 1994. Le monde de �a la decision, LAFORIA.

Working Papers Series, Number 94/20, pp. 1–26.

Porter, M., 1985. Competitive Advantage: Creating and Sus-

taining Superior Performance. Free Press, New York. pp.

33–61.

Porter, M., 2001. Strategy and the internet. Harvard Business

Review (March), 63–77.

Rappa, M., 2003. Business Models on the Web. Available at

http://ecommerce.ncsu.edu/models/models_text.html.

Rayport, J.F., Jaworski, B.J., 2001. Introduction to e-Com-

merce. McGraw-Hill, New York.

Rosenhead, J., Best, G., Parston, G., 1986. Robustness in

practice. Journal of the Operational Research Society 37 (5),

463–478.

Sammon, D., Adam, F., 2000. Towards a model of ERP

software selection––widening the debate. In: Proceedings of

the 10th Annual BIT Conference, November 2000, Man-

chester, UK.

Simon, H.A., 1960. The New Science of Management Decision.

Prentice Hall, Englewood Cliff, NJ.

Ticoll, D., Lowy, A., Kalakota, R., 1998. In: Tapscott, D.

(Ed.), Blueprint to the Digital Economy: Creating Wealth in

the Era of e-Business. McGraw-Hill, New York.

Timmers, P., 1999. Electronic Commerce: Strategies and

Models for Business-to-Business Trading. Wiley.

Van Over, D., Kavan, C.B., 1993. Adopting EDI: When and

why? Information Strategy: The Executive�s Journal 2

(Summer), 50–52.

Wise, R., Morrison, D., 2000. Beyond the exchange: The future

of B2B. Harvard Business Review (November–December),

86–96.

http://www.accenture.com/xdoc/en/ideas/isc/pdf/Surveying_the_Landscape_WP.pdf-whitepaper



http://inforge.unil.ch/aosterwa

http://ecommerce.ncsu.edu/models/models_text.html

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