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. 848 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. 852 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. 854 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. 856 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 858 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. 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Venkatesh, Determinants of perceived ease of use: integrating control, intrinsic motivation, & emotion into the technology acceptance model, Information Systems Research 11 (4), 2000, pp. 342–365. [62] V. Venkatesh, F.D. Davis, A theoretical extension of the technology acceptance model: four longitudinal field studies, Management Science 46 (2), 2000, pp. 186–204. [63] P.C. Verhoef, F. Langerak, Possible determinants of consumers’ adoption of electronic grocery shopping in The Netherlands, Journal of Retailing and Consumer Services. 8, 2001, pp. 275– 285. [64] R. Yin, Applications of Case Study Research, SAGE Publications, Thousand Oaks, California, 1993. [65] R. Yin, Case Study Research: Design and Methods, second ed., SAGE Publications, Thousand Oaks, California, 1994. 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).