Determinants of adoption of High Speed Data Services in the business market: Evidence for a combined...

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Information & Management 43 (2006) 847–860

Determinants of adoption of High Speed Data Services in the

business market: Evidence for a combined technology

acceptance model with task technology fit model

Margherita Pagani *

Management Department, Bocconi University, Via Bocconi 8, 20136 Milan, Italy

Received 3 May 2005; received in revised form 3 May 2006; accepted 10 August 2006

Available online 14 September 2006

Abstract

This paper presents a Business-Oriented Model of Factors that affect the adoption of wireless High Speed Data Services (HSDS).

We reviewed business IT acceptance literature and developed an explorative survey of a sample of twelve companies in Europe and

USA. From this, a theoretical model was created and hypotheses were formulated. Data were then collected on a sample of 1545

companies in USA and Europe. Based on these results, we developed a model that combined the key ideas of both TAM and TTF

and showed that both were necessary in predicting wireless High Speed Data Service adoption.

# 2006 Elsevier B.V. All rights reserved.

Keywords: Technology adoption model; High Speed Data Services; Task technology fit model; Wireless adoption

1. Introduction

Wireless devices today include mobile phones,

personal digital assistants (PDAs) with wireless

modems, wireless laptops, two-way pagers/short mes-

sage systems, and wireless networks. We wished to

understand the determinants influencing wireless

adoption decisions for a ‘‘mobile office’’ service based

upon Third Generation (3G) mobile telecommunication

technology that provides mobile workers with fast,

secure, convenient access to the services on corporate

networks. Plug-in PCMCIA wireless modem cards

allow existing laptop PCs and PDAs with permanent

connectivity to the corporate network via a secure

Virtual Private Network (VPN) across a mobile

operator’s network. Our study attempted to provide a

* Tel.: +39 02 58366920; fax: +39 02 58366888.

E-mail address: [email protected].

0378-7206/$ – see front matter # 2006 Elsevier B.V. All rights reserved.

doi:10.1016/j.im.2006.08.003

better theoretical understanding of the antecedents of

business acceptance and resistance to adoption of High

Speed Data Services (HSDS).

Our research questions were:

1. W
hat are the most important factors in making the
decision to adopt wireless High Speed Data

Services?

2. W
hat are the constraining factors in its adoption?
3. W
hat is the decision-making process?
After reviewing relevant literature, a three-step

methodology was developed. In the first step an

explorative survey was conducted through interviews

on 12 companies in Europe and the USA. In the second,

we formulated a research model. Finally, in the third

step, we empirically tested the model on a sample of

1545 companies (in 19 industry segments) across the

USA and five countries of Europe.

mailto:[email protected]

http://dx.doi.org/10.1016/j.im.2006.08.003

M. Pagani / Information & Management 43 (2006) 847–860848

2. Theoretical background

Our study lies at the intersection of two issues: the

technology adoption decision-making process and the

analysis of determinants of IT acceptance and utiliza-

tion by business users.

Technology adoption research has flourished in

recent years [2,17,23,32,37,44,46,58,62,63]. Currently

TAM [14,15] grounded in Fishbein and Azjen’s [20]

TRA is very popular. In the IS literature on IT adoption,

researchers have conducted studies to examine the

relationship between perceived ease of use, perceived

usefulness, and the usage of other information

technologies [11,29,43,57].

A second model of technology adoption, the task

technology fit (TTF) model [22], extends TAM by

considering how the task affects use. More specifically,

it proposed that technology adoption depended in part

on how well the new technology fitted the requirements

of a particular task. Dishaw and Strong found that TTF

was somewhat more effective than TAM for predicting

use in work-related tasks; however, their study also

concluded that a combination into one extended model

was superior to either.

Although there are numerous studies in these fields

[8,27,30,31,34,35,36,38,53,56,59,61], previous works

have focused mainly on the adoption of products and

technology [4,19]. In contrast, the perspective on

wireless High Speed Data Services in the business

market has not been discussed: few studies have

discussed factors related to the adoption of telecom-

munications [25] and client server technology [13].

Studies on reasons that small business owner/

managers do or do not adopt IT and e-commerce

technologies [12,60] have highlighted both inhibitors

and facilitators. Small business adoption has been

discussed as depending on characteristics of the

decision maker, IS, organization, and the environment.

Lack of speed is a barrier, as mobile data

technologies are slow and hence inefficient [54].

Another barrier is the perception of a lack of

standardized IT environment for developing mobile

data applications [5,28]. Security [9,48], limited

bandwidth, higher usage costs, increased latency, a

susceptibility to transmission noise, and the degree of

call dropouts [18,33].

Telecommunication companies have been making

enormous investments in new wireless technologies and

they are looking for killer applications to provide pay

offs. Several empirical studies took place to find out

possible applications [49–52,55,64] but these have not

yet been implemented [40,42].

There is a need for more substantive, theory-based

research, creating a more in-depth understanding of

factors influencing adoption of wireless technologies by

companies.

3. The explorative survey

3.1. Methodology

The explorative survey was conducted by interview-

ing personnel in 12 companies (five in USA and seven in

Europe) having different size and ownership character-

istics. The case study [55,64,65] interviews were

conducted in years 2003 and 2004 with the CIO or

equivalent executive, and one or two managers in

charge of telecommunications. This resulted in a total of

36 interviews that helped us understand the determi-

nants important in the adoption process. Multiple

responses from each respondent were allowed. Deter-

minants were spontaneously stated and evaluated by

respondents through a 4-point Likert scale (where 3

meant high importance, 2 moderate importance, 1 low

importance, and 0 no importance).

The specific criteria for company selection was to

provide a mixture of high tech versus manufacturing;

public versus private ownership; companies with a

global presence; and at least one whose future was

closely tied to broadband communication (a global

entertainment company).

The companies belonged to eleven industries: (1)

distributor of industrial products; (2) software vendor

and services; (3) medical products manufacturing; (4)

networking and telecom hardware; (5) entertainment;

(6) media broadcasting company; (7) government and

legal management company; (8) insurance company;

(9) car manufacturer; (10) IT service company; and (11)

system technology.

The construct validity was proven by consulting

multiple sources (interviews and documents) and

review of the case study transcripts. Internal validity

was tested by constructing a detailed research frame-

work ahead of time. External validity was limited, since

it was an exploratory study. Reliability was based on a

detailed case study protocol that documented the

scheduling, interview procedures, recording, follow-

ups, questions, and summary database.

The research framework consisted of factors under

the groupings of wireless adoption, and utilization. The

wireless utilization factors were: the number of mobile

devices deployed, extent of anticipated future deploy-

ment, uses of mobile phones, and anticipated future

uses.

M. Pagani / Information & Management 43 (2006) 847–860 849

3.2. Emerging explanatory variables

Respondents were asked to state the most important

factors that influenced their adoption process and to rate

their relative importance on a 4-point Likert scale. Two

main categories of explanatory variables influenced

their decision (see Fig. 1):

- T
echnological: reliability, security, costs, scalability,
establishing data connection, supportability, high

connectivity, productivity, digital standards, band-

width, coverage.

- N
on-technological: outside perception, ability to
provide service to customer, ease of use by employees,

regulation, and additional revenues/opportunity costs.

The most important attributes for adoption (with a

value above 2) were reliability, security, costs, outside

perception. Security’s prominence was consistent with

other studies of mobile technology. Security and

reliability are not present in traditional adoption

models, but may have become more significant in the

years since those models were first introduced.

Reliability is a highly rated attribute because it is so

intertwined with coverage and ability to provide

continual service. The attribute of reliability and cost

were rated at medium to high. Data connectivity was not

an attribute in traditional models, which preceded

widespread web use in businesses [47]. The ability to

provide service to the customers is consistent with TAM

and subsequent studies [1,24,39].

The software provider put high emphasis on

productivity, a factor not present in our theoretical

model. Almost all companies stated the importance of

Fig. 1. Average attrib

standards. This is consistent with results from non-

academic literature which has stated that lack of

standard is one of the deterrents of technology adoption.

Tables 1 and 2 show the correlations among

attributes; there were no correlations among non-

technology explanatory variables but high correlations

among technology explanatory variables.

3.3. Results and discussion: psychometric

properties of the instruments

Factor Analysis was performed on the explanatory

variables in order to establish their suitability for

performing the multivariate analysis. A Principal

Components Analysis (PCA) was used to examine

the factor structure and help the measures conform to

recommended levels of reliability. The results were

based on sets of variables, guided by conceptual and

practical considerations: (a) the acceptance of factor

loadings of approximately .50 and above—this level is

considered practically significant [26], (b) most of the

cross-loadings falling below .20. The internal consis-

tency of the instruments was further tested via reliability

analyses (Cronbach’s Alpha). High communality values

were observed for all variables indicating that the total

amount of variance that an original variable shares with

all other variables is high. Table 3 shows the summaries

of the results of PCA factors and item loadings of ICT

usage.

Reliability analysis showed the Cronbach’s Alpha

values: data connectivity (.88), technology suitability

(.75), customer satisfaction (.58). Except for customer

satisfaction, where Cronbach’s Alpha (.58) can be

rounded up to .60, the reliability test results show values

ute importance.

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0Table 1

Correlations: technology explanatory variables

Cost Reliability Bandwidth Security Scalability Connectivity

to web

Digital

standards

Technology

suitability

Supportability Productivity Coverage

Operational Costs Pearson Correlation 1

Sig. (2-tailed)

N 36

Reliability Pearson Correlation �.055 1

Sig. (2-tailed) .865

N 36 36

Bandwidth Pearson Correlation �.440 .170 1

Sig. (2-tailed) .152 .598

N 36 36 36

Security Pearson Correlation �.301 �.165 .655** 1

Sig. (2-tailed) .342 .609 .021

N 36 36 36 36

Scalability Pearson Correlation �.128 �.080 .619* .794** 1

Sig. (2-tailed) .692 .85 .032 .002

N 36 36 36 36 36

Always on

connectivity

Pearson Correlation �.292 .102 .567 .714** .622* 1

Sig. (2-tailed) .358 .752 .055 .009 .031

N 36 36 36 36 36 36

Digital standards Pearson Correlation �.150 .750** .412 �.069 .093 .045 1

Sig. (2-tailed) .643 .005 .183 .832 .773 .889

N 36 36 36 36 36 36 36

Establishing data

connection

Pearson Correlation �.249 .518 .274 �.097 .015 �.050 .756** 1

Sig. (2-tailed) .436 .084 .389 .763 .962 .876 .004

N 36 36 36 36 36 36 36 36

Supportability Pearson Correlation �.212 �.025 �.125 �.065 �.059 �.598* �.069 .149 1

Sig. (2-tailed) .507 .938 .699 .840 .856 .040 .832 .644

N 36 36 36 36 36 36 36 36 36

Workforce

productivity

Pearson Correlation .281 �.228 �.225 �.388 �.361 �.418 �.323 �.153 .235 1

Sig. (2-tailed) .377 .475 .482 .213 .250 .176 .306 .636 .462

N 36 36 36 36 36 36 36 36 36 36

Coverage Pearson Correlation �.237 .233 �.365 �.653* �.344 �.418 .115 .392 .327 .196 1

Sig. (2-tailed) .459 .467 .244 .021 .274 .176 .722 .208 .300 .541

N 36 36 36 36 36 36 36 36 36 36 36

* Correlation is significant at the 0.05 level (2-tailed).** Correlation is significant at the 0.01 level (2-tailed).


Table 2

Correlations: non-technology explanatory variables

Ease of use Ability to provide

service to customer

Outside

perception

Regulation Opportunity

costs

Employees ease of use Pearson Correlation 1

Sig. (2-tailed)

N 36

Ability to provide service

to customer

Pearson Correlation �.067 1

Sig. (2-tailed) .837

N 36 36

Outside perception Pearson Correlation �.111 .124 1

Sig. (2-tailed) .732 .701

N 36 36 36

Regulation Pearson Correlation .143 �.053 .342 1

Sig. (2-tailed) .657 .870 .277

N 36 36 36 36

Opportunity costs/revenues Pearson Correlation .105 �.411 .302 .438 1

Sig. (2-tailed) .746 .184 .341 .154

N 36 36 36 36 36

exceeding .60 recommended by Hair et al. as the lower

limit of acceptability, ensuring that the items grouping

for the respective variables are reliable. Only workforce

efficiency and workforce productivity show low values.

The mean of components showing internal consistency

is for data connectivity (F1) 1.79 (high), technology

suitability (F2) 1.31 (medium), customer satisfaction

(F3) 1.67 (high).

Table 3

Principal component analysis

Component

F1 F2

Cronbach’s Alpha values .876 .752

Bandwidth .874 7.1E�02

Security .795 �.384

Scalability .819 �.160

Always on connectivity .812 �.266

Additional revenues/opportunity costs .650 .309

Outside perception .580 6.5E�02

Regulation .676 �182

Digital standards .362 .812Establishing data connection .193 .852Reliability .166 .739Coverage �.399 .606Supportability �.288 .272

Ability to provide service to customer �.176 .140

Employees ease of use .146 .425

Operational costs �.433 �.229

Workforce productivity �448 �.170

Extraction method: principal component analysis. Five factors extracted:

satisfaction; (F4) workforce efficiency; (F5) workforce productivity.

The values in bold signifies loadings for each variable.

4. Research hypothesis

The theoretical framework had to define the linkages

between beliefs about adopting and using wireless

technology, while the explorative survey provided the

underlying structure for the theoretical model. The

proposed conceptual model of wireless technology

adoption for this study is shown as Fig. 2.

Communalities

F3 F4 F5

.575 .245

5.5E�03 .137 .244 .848

.243 .177 �.305 .964

.141 .247 �.144 .799

�8.2E�02 �.332 �.178 .878

�.340 2.7E�03 .373 .773

.340 �.527 .124 .749

346 299 491 .941

�.323 4.1E�02 3.8E�02 .898

.111 6.2E�02 �5.2E�02 .783

�.293 �8.5E�02 �9.3E�02 .675

.339 �.351 .190 .801

.684 .494 �2.1E�02 .880

.728 �.213 �.343 .743

.102 .707 �.253 .777

�.560 .398 �5.8E�02 .716

.246 .169 .718 .831

(F1) data connectivity; (F2) technology suitability; (F3) customer


Fig. 2. Research model with directions of hypothesized relationships.

All of the companies were considering web-based

connectivity for future adoption, they also realized that

they would not reach the full benefits of it until the high

bandwidth capabilities of Third Generation were

available, particularly for streaming video. As the uses

of this technology become more complex and web

driven, regulation may be more important. We therefore

state the following hypotheses:

Hypothesis 1. Data connectivity is positively related to

interest to adopt;

Hypothesis 2a. Data Speed is positively related to

interest to adopt;

Hypothesis 2b. Data Speed is positively related to data

connectivity;

Respondents declare technology suitability as a factor

influencing their adoption of wireless services. This

factor is influenced by geographic coverage, reliability,

suitability to establish data connection, digital standards

and the search for a combination PIM/wireless capability.

Therefore we hypothesized that:

Hypothesis 3. Technology suitability is positively

related to interest to adopt;

Workforce productivity is consistent with the

importance in TAM of usefulness. In accordance with

this model we hypothesized:

Hypothesis 4. Workforce productivity is positively


Customer satisfaction and workforce efficiency are

likely to become more important in the future and also

more complex, requiring greater user support. Their

importance is related to the ease of use factor stressed in

TAM models. A body of empirical research already

indicates a significant association between IT and

behavioral intention and between IT and usefulness.

Therefore we hypothesized:

Hypothesis 5. Customer satisfaction is positively


Hypothesis 6a. Workforce efficiency is positively


Hypothesis 6b. Workforce efficiency is positively

related to workforce productivity;

Hypothesis 6c. Workforce efficiency is positively

related to customer satisfaction;

Hypothesis 7. Interest is positively related to intention

to adopt.

The TTF model suggested that individuals should

consider beliefs about perceived usefulness and

perceived ease of use, and also the extent to which

the technology met their task needs and individual


abilities. The following hypothesis were therefore

proposed:

Hypothesis 8. Task influences workforce productivity

and attribute importance;

Hypothesis 9. Task influences the share of preference;

Hypothesis 10. The combined TTF/TAM predicts the

intention to adopt.

5. Testing The hypotheses

5.1. Sample

The quantitative analysis was conducted in 2004

through a phone questionnaire (conducted by Lucent

Technologies) on a sample of 1545 companies across

the USA and five countries in Europe. Market

perceptions were obtained from interviews with

Telecom/IT managers from major corporations.

Respondents were required to be those who make or

influence decisions for a minimum of two of the

following areas: (a) MIS/IT/network; (b) desk top/PC/

laptop systems; (c) landline voice/data; (d) mobile

voice; (e) mobile data; (f) e-mail.

Fig. 3. Utility of a

All companies belonged to nineteen market segments

(banking, insurance, financial, manufacturing, utilities,

public sector, maintenance, service of enterprise, service

of consumer, other service, hospitals, pharmaceutical,

research, health care, transportation, media and com-

munication, wholesale retail, education, other) and have

at least thirty mobile or remote data users.

We tested all the factors emerging in the proposed

conceptual model: (F1) data connectivity; (F2) technol-

ogy suitability; (F3) customer satisfaction; (F4) work-

force efficiency; (F5) workforce productivity; (F6) data

speed; (F7) interest to adopt.

We selected also a sample of critical explanatory

variables, from the previous explorative survey, to test

their influence on operational costs; opportunity costs/

sales revenues; always on connectivity; cost of access;

coverage. For each factor and selected explanatory

variable each respondent was asked to evaluate the

relative importance on a 1–10 Likert scale.

5.2. Methodology

The methodology is based on a probabilistic ideal

vector model [6,7,16,21]. Deterministic points for

alternatives and random ideal vectors for industry

segments were used for explaining and predicting choice

behavior in a low dimensional attribute space where the

ll segments.

i


Fig. 4. Barriers to the adoption—importance of attributes (scale 1–10).

same model formulation was employed for parameter

estimation and market simulation. The first objective of

the analysis was to verify for each industry segment the

most important factors influencing the adoption process.

Table 4

Dendogram using average linkage (between groups)

The discriminant function can be written as:

UðXtÞ ¼X

wtxi þ w0 (1)


where U(Xt) is a linear combination of the utility related

to component xi for segment t; wt is the weighted vector

for segment t; w0 is the bias or threshold weight.

Each input feature value xi is multiplied by its

corresponding weight wi. The output unit sums these

and emits +1 if wixi þ w0 � marginal utility or �1

otherwise. A two-category threshold weight linear

classifier implemented the decision rule: decide to adopt

the service if U(Xt) � marginal utility industry segment

t and not to adopt otherwise.

6. Prioritizing customer needs

The next phase involves identifying the data

attributes that are most and least important to all

customers; this requires understanding and analysis of

customer needs in an industry segment. The importance

of an individual attribute is determined by the span of

the utility levels for each attribute, compared to utility

spans for other attributes.

Let the random variable U(Xt) be the utility assigned

by companies of segment t. Then the High Speed Data

Fig. 5. Industry seg

Services’ total utility for each segment is defined as the

sum of the utility values of the attribute levels that have

used to describe it.

Let Utj denote the utility value assigned to service j

by the industry segment t. The U’s denote scale values

or strict utilities, which summarize the desirability of

the alternatives. These scale values are functions of the

attributes of the alternatives, interacting with the

characteristics of the respondent segment, and possibly

with features of the choice set as a whole. The scale

values are assumed to have an additively separable

linear form:

UtiðXÞ ¼ xi1w1 þ xi2w2 þ . . .þ xi jw j (2)

where X is a fully specified functions of measured

attributes and characteristics and/or self-explicated

scales of service aspect and the w’s are importance

weight parameters that must be estimated. The w’s

importance weight parameter is in the range of 1–10.

Findings emerging from the quantitative analysis

(Fig. 3) showed that the workforce efficiency was the

most important factor influencing the decision to

ment plotter.


Fig. 6. Importance of workforce efficiency, customer satisfaction, and additional sale revenues.

Fig. 7. Standardized parameter estimates.


Table 5

Summary of research results

Hypotheses Result

H1 Data connectivity has a positive direct effect on interest to adopt Supported

H2a Data speed has a positive direct effect on interest to adopt Supported

H2b Data speed has a positive direct effect on data connectivity Supported

H3 Technology suitability has a positive direct effect on interest to adopt Supported

H4 Workforce productivity has a positive direct effect on interest to adopt Supported

H5 Customer satisfaction has a positive direct effect on interest to adopt Supported

H6a Workforce efficiency has a positive direct effect on interest to adopt Not supported

H6b Workforce efficiency has a positive direct effect on workforce productivity Not supported

H6c Workforce efficiency has a positive direct effect on customer satisfaction Not supported

H7 Level of Interest has a positive direct effect on intention to adopt Supported

H8 Task influences workforce productivity and attribute importance Supported

H9 Task influences the share of preference Supported

H10 The combined TTF/TAM predicts the intention to adopt Supported

develop High Speed Data Services for insurance

companies, while customer satisfaction was the most

important factor for companies which offer services for

consumers. In the pharmaceutical sector additional sale

revenues represented the main motivation to adopt high

speed wireless technology.

Results related to the perceived barriers that

influence the decision to adopt high speed data

technology (Fig. 4) showed that coverage was a critical

issue for research companies, while those in the health

care field perceive data connectivity as critical.

6.1. Segmenting customers according to their needs

In any group, different companies will find different

attributes important. Needs-based segmentation

attempts to understand these differences by grouping

together companies who assign similar levels of

importance to the same ones [3]. The benefit of this

approach over mass marketing is that it enables

different services to be developed to meet the needs

of different segments.

The dendogram obtained by applying a hierarchical

cluster analysis using average linkage between groups

(Square Euclidean distances) (Table 4) gives the

distances or similarities between items. It showed that

the first cluster was composed of service enterprises

(education, manufacturing, media and communication,

etc.). The second cluster included financial, transporta-

tion, public sector, and wholesale retail, while

pharmaceutical and research companies were the most

dissimilar.

Fig. 5 shows segments according to three factors:

data connectivity (F1); technology suitability (F2);

customer satisfaction (F3).

Finally we consider three attributes characterized by

high extraction communalities:

1. w
orkforce efficiency (.95);
2. c
ustomer satisfaction (.95);
3. a
dditional sales revenues (.96).
Three main clusters resulted (see Fig. 6).

7. Conclusions

The motivation for this work was the assumption that

the fit between task characteristics and technology

would impact the adoption process. We extended

previous results of TAM by linking it to TTF theory. The

resulting research model and emerging findings have

several implications.

Standardized parameter estimates for the revised

model were shown in Fig. 7, where the decision to

deploy was significantly predicted by interest and

evaluation (b = .807, p < .01); this was significant as far

as data speed (b = .617, p < .05), data connectivity

(b = .364), technology suitability (b = .365), customer

satisfaction (b = .077) and workforce productivity

(b = .282) were concerned. Data connectivity was

predicted by both data speed (b = .541) and always

on connectivity (b = .69, p < .05).

Workforce productivity is positively related to

interest (b = .282). This result was consistent with

previous studies on TAM. If business users perceive

High Speed Data Service to be useful, they will be more

likely to adopt the innovation. On the other hand,

workforce efficiency was not significantly related to

interest (H7), contradicting expectations. This finding

agreed with the original TAM and studies focused on


Internet banking adoption [10] and online shopping, but

it contradicted the results of many previous studies

[41,45], where ease of use was a significant determinant

of intention to use computer technology.

Findings showed that a combined TTF/TAM was

also appropriate. The adoption model needed to

consider how well the new technology fits the

requirements of a particular task. Table 5 shows a

summary of research results.

The research model defined in Fig. 7 had several

implications. The constructs in the model should be

embedded as one part of a larger complex of contextual

variables associated with task technology fit, organiza-

tional task environments, individual, group, organiza-

tional performance, and customer satisfaction. The

model could also evolve by considering measures for

task characteristics, such as decision-making speed, and

decision-making in high-velocity environments.

8. Managerial implications

The study contributes to diffusion research by using

detailed primary data about firms and institutions in

several sectors and comparing the influences that

affected the awareness and adoption of wireless data

technologies. Our intent was to provide tools for

analyzing the demand factors that drive adoption of

wireless services in the corporate market by taking

specific examples from case study research and an

explorative quantitative survey, examining them in a

systematic and comparative manner.

Results revealed that awareness or interest plays a

significant role in influencing intention to adopt

wireless services. Data speed and technology suit-

ability were perceived as important determinants of

adoption by all segments. Data connectivity played an

important role in particular for research and banking.

Customer satisfaction was the most important attribute

for companies belonging to the pharmaceutical seg-

ment or companies aimed to provide services. Work-

force productivity was pursued by insurance

companies.

For practitioners, our findings highlight the need to

pay close attention to both organizational task

environments and the users’ needs for high speed data

to further support their decision-making tasks. We

found they need to consider data connectivity aspects,

customer satisfaction requirements and workforce

productivity when deciding whether to redesign or

discontinue current systems or support policies. They

also need to consider whether to redesign task support to

take better advantage of IT potential. To do so, they

must understand the changing nature of tasks and apply

task-oriented analysis.

Our findings can serve as the basis for a strong

diagnostic tool for evaluating whether wireless IS and

related services are meeting needs. Such evaluations

should specifically identify the gaps between wireless

systems and support capabilities and needs.

Acknowledgments

The author is very grateful to the Senior Editor Prof.

Edgar H. Sibley and the three anonymous reviewers for

their valuable suggestions and comments which

enhanced the presentation of this research.

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Margherita Pagani is assistant pro-

fessor of Management at Bocconi

University (Italy) and head

researcher for New Media&Tv-lab

inside I-LAB Centre for Research

on the Digital Economy of Bocconi

University. She is associate editor of

Journal of Information Science and

Technology JIST. She was visiting

scholar at Sloan—MIT (Massachu-

setts Institute of Technology) and

visiting professor at Redlands Uni-

versity (California). She worked

with RAI Radiotelevisione Italiana

and as a member of the Workgroup ‘‘Digital Terrestrial’’ for the

Ministry of Communications in Italy. She is the author of the books

‘‘La Tv nell’era digitale’’ (EGEA 2000), ‘‘Multimedia and Interactive

Digital TV: Managing the Opportunities Created by Digital Conver-

gence’’ (IRM Press 2003), ‘‘Full Internet mobility in a 3G-4G

environment: managing new business paradigms’’ (EGEA 2004).

She has edited the books ‘‘Mobile and Wireless Systems beyond

3G: managing new business opportunities (IPG 2004) and ‘‘Encyclo-

pedia of Multimedia Technology and Networking’’ (IRM Press 2005).

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