Information & Management 43 (2006) 847–860
www.elsevier.com/locate/im
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 message 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: margherita.pagani@unibocconi.it.
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. What are the most important factors in making the
decision to adopt wireless High Speed Data
Services?
2. What are the constraining factors in its adoption?
3. What 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.
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M. Pagani / Information & Management 43 (2006) 847–860
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 utilization 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 telecommunications [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 interviewing personnel in 12 companies (five in USA and seven in
Europe) having different size and ownership characteristics. 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 determinants important in the adoption process. Multiple
responses from each respondent were allowed. Determinants 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 framework 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, followups, 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 deployment, uses of mobile phones, and anticipated future
uses.
M. Pagani / Information & Management 43 (2006) 847–860
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):
- Technological: reliability, security, costs, scalability,
establishing data connection, supportability, high
connectivity, productivity, digital standards, bandwidth, coverage.
- Non-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
849
standards. This is consistent with results from nonacademic 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 nontechnology 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 consistency 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
Fig. 1. Average attribute importance.
850
Table 1
Correlations: technology explanatory variables
Cost
Operational Costs
Reliability
Bandwidth
Pearson Correlation
Sig. (2-tailed)
N
1
Reliability
Pearson Correlation
Sig. (2-tailed)
N
.055
.865
36
1
Bandwidth
Pearson Correlation
Sig. (2-tailed)
N
.440
.152
36
.170
.598
36
1
Pearson Correlation
Sig. (2-tailed)
N
.301
.342
36
.165
.609
36
.655**
.021
36
Scalability
Pearson Correlation
Sig. (2-tailed)
N
Connectivity
to web
Digital
standards
Technology
suitability
Supportability
Productivity
.128
.692
36
36
.080
.85
36
36
.619
.032
36
*
1
36
.794**
.002
36
1
36
.622*
.031
36
36
.069
.832
36
.093
.773
36
.045
.889
36
1
.097
.763
36
.015
.962
36
.050
.876
36
.756**
.004
36
36
.069
.832
36
.149
.644
36
36
Pearson Correlation
Sig. (2-tailed)
N
.292
.358
36
.102
.752
36
.567
.055
36
.714
.009
36
Digital standards
Pearson Correlation
Sig. (2-tailed)
N
.150
.643
36
.750**
.005
36
.412
.183
36
Pearson Correlation
Sig. (2-tailed)
N
.249
.436
36
.518
.084
36
.274
.389
36
**
1
36
*
1
Supportability
Pearson Correlation
Sig. (2-tailed)
N
.212
.507
36
.025
.938
36
.125
.699
36
.065
.840
36
.059
.856
36
.598
.040
36
Workforce
productivity
Pearson Correlation
Sig. (2-tailed)
N
.281
.377
36
.228
.475
36
.225
.482
36
.388
.213
36
.361
.250
36
.418
.176
36
.323
.306
36
.153
.636
36
.235
.462
36
1
.344
.274
36
.418
.176
36
.115
.722
36
.392
.208
36
.327
.300
36
.196
.541
36
Coverage
*
**
Pearson Correlation
Sig. (2-tailed)
N
Coverage
36
Always on
connectivity
Establishing data
connection
Scalability
M. Pagani / Information & Management 43 (2006) 847–860
Security
Security
.237
.459
36
.233
.467
36
Correlation is significant at the 0.05 level (2-tailed).
Correlation is significant at the 0.01 level (2-tailed).
.365
.244
36
.653
.021
36
*
1
36
1
36
M. Pagani / Information & Management 43 (2006) 847–860
851
Table 2
Correlations: non-technology explanatory variables
Ease of use
Employees ease of use
Ability to provide
service to customer
Outside
perception
Regulation
Pearson Correlation
Sig. (2-tailed)
N
1
Pearson Correlation
Sig. (2-tailed)
N
.067
.837
36
1
Pearson Correlation
Sig. (2-tailed)
N
.111
.732
36
.124
.701
36
1
Regulation
Pearson Correlation
Sig. (2-tailed)
N
.143
.657
36
.053
.870
36
.342
.277
36
1
Opportunity costs/revenues
Pearson Correlation
Sig. (2-tailed)
N
.105
.746
36
.411
.184
36
.302
.341
36
.438
.154
36
Ability to provide service
to customer
Outside perception
Opportunity
costs
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).
36
36
36
1
36
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.
Table 3
Principal component analysis
Component
Communalities
F1
F2
F3
F4
Cronbach’s Alpha values
.876
.752
.575
.245
Bandwidth
Security
Scalability
Always on connectivity
Additional revenues/opportunity costs
Outside perception
Regulation
Digital standards
Establishing data connection
Reliability
Coverage
Supportability
Ability to provide service to customer
Employees ease of use
Operational costs
Workforce productivity
.874
.795
.819
.812
.650
.580
.676
.362
.193
.166
.399
.288
.176
.146
.433
448
7.1E02
.384
.160
.266
.309
6.5E02
182
.812
.852
.739
.606
.272
.140
.425
.229
.170
5.5E03
.243
.141
8.2E02
.340
.340
346
.323
.111
.293
.339
.684
.728
.102
.560
.246
.137
.177
.247
.332
2.7E03
.527
299
4.1E02
6.2E02
8.5E02
.351
.494
.213
.707
.398
.169
F5
.244
.305
.144
.178
.373
.124
491
3.8E02
5.2E02
9.3E02
.190
2.1E02
.343
.253
5.8E02
.718
.848
.964
.799
.878
.773
.749
.941
.898
.783
.675
.801
.880
.743
.777
.716
.831
Extraction method: principal component analysis. Five factors extracted: (F1) data connectivity; (F2) technology suitability; (F3) customer
satisfaction; (F4) workforce efficiency; (F5) workforce productivity.
The values in bold signifies loadings for each variable.
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M. Pagani / Information & Management 43 (2006) 847–860
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 4. Workforce productivity is positively
related to interest to adopt;
Hypothesis 1. Data connectivity is positively related to
interest to adopt;
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 2a. Data Speed is positively related to
interest to adopt;
Hypothesis 5. Customer satisfaction is positively
related to interest to adopt;
Hypothesis 2b. Data Speed is positively related to data
connectivity;
Hypothesis 6a. Workforce efficiency is positively
related to interest to adopt;
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 6b. Workforce efficiency is positively
related to workforce productivity;
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 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
M. Pagani / Information & Management 43 (2006) 847–860
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.
853
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 communication, 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) technology suitability; (F3) customer satisfaction; (F4) workforce 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
Fig. 3. Utility of all segments.
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M. Pagani / Information & Management 43 (2006) 847–860
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:
UðX t Þ ¼
X
wti xi þ w0
(1)
M. Pagani / Information & Management 43 (2006) 847–860
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 wi xi 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
855
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 U tj 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:
Uit ðXÞ ¼ xi1 w1 þ xi2 w2 þ . . . þ xi j w 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
Fig. 5. Industry segment plotter.
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M. Pagani / Information & Management 43 (2006) 847–860
Fig. 6. Importance of workforce efficiency, customer satisfaction, and additional sale revenues.
Fig. 7. Standardized parameter estimates.
M. Pagani / Information & Management 43 (2006) 847–860
857
Table 5
Summary of research results
H1
H2a
H2b
H3
H4
H5
H6a
H6b
H6c
H7
H8
H9
H10
Hypotheses
Result
Data connectivity has a positive direct effect on interest to adopt
Data speed has a positive direct effect on interest to adopt
Data speed has a positive direct effect on data connectivity
Technology suitability has a positive direct effect on interest to adopt
Workforce productivity has a positive direct effect on interest to adopt
Customer satisfaction has a positive direct effect on interest to adopt
Workforce efficiency has a positive direct effect on interest to adopt
Workforce efficiency has a positive direct effect on workforce productivity
Workforce efficiency has a positive direct effect on customer satisfaction
Level of Interest has a positive direct effect on intention to adopt
Task influences workforce productivity and attribute importance
Task influences the share of preference
The combined TTF/TAM predicts the intention to adopt
Supported
Supported
Supported
Supported
Supported
Supported
Not supported
Not supported
Not supported
Supported
Supported
Supported
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, transportation, 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. workforce efficiency (.95);
2. customer satisfaction (.95);
3. additional 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
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M. Pagani / Information & Management 43 (2006) 847–860
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, organizational task environments, individual, group, organizational 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 suitability 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 segment or companies aimed to provide services. Workforce 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 professor 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 (Massachusetts Institute of Technology) and
visiting professor at Redlands University (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 Convergence’’ (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 ‘‘Encyclopedia of Multimedia Technology and Networking’’ (IRM Press 2005).