Towards a heuristic frame for transferring e-government technology

Government Information Quarterly 31 (2014) 195–207

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Government Information Quarterly journal homepage: www.elsevier.com/locate/govinf

Review

Towards a heuristic frame for transferring e-government technology Gabriel Marcuzzo do Canto Cavalheiro a,b,⁎, Luiz Antonio Joia a a b

Brazilian School of Public and Business Administration, Getulio Vargas Foundation, Rio de Janeiro, Brazil Getulio Vargas Foundation, Praia de Botafogo 190, Room 526, CEP 22253-900 Rio de Janeiro, Brazil

a r t i c l e

i n f o

Available online 9 January 2014 Keywords: e-Government Technology transfer Information systems Knowledge management Developing countries

a b s t r a c t This article addresses challenges in accomplishing technology transfer process involving the adaptation and implementation of e-government applications from a donor country to a recipient country. Here it is claimed that prior e-government research has overlooked existing technology transfer literature from the field of knowledge management. This work is aimed at addressing the underlying issues associated with the transfer of e-government technology, given different characteristics of donor and recipient organizations in terms of the socio-economic context and the dynamics of the technological infrastructure. Based on a review, interpretation, and synthesis of a broad range of both technology transfer, e-government and knowledge management literature, we extend the Information Technology Transfer Life-Cycle Model, as this well-known model was derived entirely based on empirical evidence. To this end, we propose a heuristic frame for e-government technology transfer. Finally, five propositions accrued from both the literature review and the proposed heuristic frame are set forth to be further tested, in order to better understand the process dynamics of e-government technology transfer between countries. © 2013 Elsevier Inc. All rights reserved.

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Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Research method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conceptual foundation of e-government technology transfer . . . . . . . . . . 3.1. Defining e-government applications . . . . . . . . . . . . . . . . . . 3.2. Challenges for e-government applications . . . . . . . . . . . . . . . 3.3. e-Government in developing countries . . . . . . . . . . . . . . . . 3.4. e-Government technology transfer . . . . . . . . . . . . . . . . . . 3.5. Information Technology Transfer Life-Cycle Model . . . . . . . . . . . Technology transfer from knowledge management perspective . . . . . . . . . 4.1. Technology as a commodity . . . . . . . . . . . . . . . . . . . . . 4.2. Technology transfer concepts . . . . . . . . . . . . . . . . . . . . . 4.3. Common barriers to technology transfer . . . . . . . . . . . . . . . . 4.4. Examining existing technology transfer models . . . . . . . . . . . . . 4.4.1. Bar-Zakay model of technology transfer . . . . . . . . . . . . 4.4.2. The Proper TT model . . . . . . . . . . . . . . . . . . . . 4.4.3. Role shifting model of technology transfer . . . . . . . . . . . 4.4.4. Stage-Gate model for technology transfer . . . . . . . . . . . 4.4.5. Transnational public sector knowledge networks (TPSKN) model 4.5. Analysis of TT models . . . . . . . . . . . . . . . . . . . . . . . . Towards a heuristic frame for e-government technology transfer . . . . . . . . 5.1. Perception of Problem . . . . . . . . . . . . . . . . . . . . . . . . 5.2. Choice of Technology . . . . . . . . . . . . . . . . . . . . . . . . 5.3. Purchase and Installation . . . . . . . . . . . . . . . . . . . . . . . 5.4. Technological, Managerial & Organizational Capability of the Recipient . .

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⁎ Corresponding author at: Getulio Vargas Foundation, Praia de Botafogo 190, Room 526, CEP 22253-900 Rio de Janeiro, Brazil. E-mail address: [email protected] (G.M.C. Cavalheiro). 0740-624X/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.giq.2013.09.005

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5.5. Adaptation . . . . . . 5.6. Adoption . . . . . . . 5.7. Diffusion and Innovation 5.8. Feedback to Donor . . . 5.9. Contextual Distances . . 5.10. Unplanned Elements . . 6. Conclusions . . . . . . . . . References . . . . . . . . . . . .

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1. Introduction As many scholars have pointed out, governments throughout the world are subject to increasing pressure to improve efficiency and effectiveness of their operations. Citizens and businesses are demanding faster delivery of public services and much better information (Ciborra, 2003; Ongaro, 2004; Osmo, 2008; Stanforth, 2006). This motivates governments to attempt to offer better public services while spending less at the same time. In this way, information and communication technologies (ICTs) have been largely used in the public sector for more than fifty years. The advent of the internet has given this usage a new name—e-government—and it has also accelerated the diffusion of e-government applications worldwide (Heeks, 2004). Beyond the technical characteristics of the ICT artifact, Stanforth (2006) defines e-government as the socio-technical arena within which ICT is being applied to organize public management in order to increase efficiency, transparency, accessibility, and responsiveness to citizens. Although governments are traditionally considered more conservative entities, slower to adopt new initiatives than players in the business realm, various authors recognize that there are many opportunities for developing e-government applications to providing better public services (Ciborra, 2003). Given the scale and complexity of their operations, public organizations are characterized by their extensive use of ICT. In this respect, there is a widespread consensus that knowledge about e-government applications has turned into a critical resource for public organizations (Stanforth, 2006), increasing strongly government expenditure on ICT throughout the world (Heeks, 2004). Nevertheless, significant variations can be observed with respect to the maturity levels of e-government practices in developed and developing countries. As a consequence of this operational gap, existing literature recognizes the increasing potential of collaboration as a means of reducing this gap (Ciborra, 2003; Heeks, 2002; Nhampossa, 2005; Stanforth, 2006). In essence, the maturity level of management and information and communication technology in developing countries is often low. Additionally, it is widely known that government bodies of different countries are characterized by significant variations in terms of service scope, quality and coverage, as the nature of the operational challenges are directly dependent upon the maturity of their business processes (Heeks, 2004). Consequently, how to internalize an organization's external e-government technology and create value out of it has become a crucial issue to government bodies of developing countries. Essentially, from a knowledge management perspective, technology transfer (TT) is considered a central instrument to tackle the necessity of different organizations to internalize external technologies (Gupta & Govindarajan, 2000). According to Jagoda (2007), TT refers to the process through which organizations learn from each other's experience and adapt all or some of the technology acquired. In this context, TT across national border is known as international technology transfer (Gupta & Govindarajan, 2000). TT has become an important phenomenon as organizations realize that it is not possible to solely rely on their in-house experience. Accordingly, the execution of TT programs has become a critical strategy for bringing in complementary capabilities and resources from external sources (Choo & Johnston, 2004; Darr &

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Kurtzberg, 2000). In this way, various scholars suggest that TT programs can be employed to achieve significant organizational change (Gibson & Smilor, 1991; Gupta & Govindarajan, 2000). However, accomplishing TT programs in the context of e-government initiatives is considered a complex challenge. According to Gibson and Smilor (1991), TT is a complex, difficult process even when it occurs across different functions within a single organization and the challenges are magnified when organization's boundaries are crossed. Furthermore, e-government systems are heterogeneous, composed of a number of disciplines and a number of sub-systems for collecting, storing, and reporting data. Hence, the e-government TT process is conceived to occur in a defined and mutant social space that involves regular collaboration with government bodies of different countries, or international organizations (Heeks, 2002). Heeks (2006) also points out that e-government TT is challenging because it requires complex customization between technology and the implementation context in developing countries. Yet, despite the complexity associated with TT environment, various developing countries are in the process of carrying out e-government technology transfer programs to strengthen their operations through technical cooperation with more advanced countries (Nhampossa, 2005; Stanforth, 2006). As such, this article concentrates on the challenges involved in transferring e-government technology from advanced government agencies in developed countries to government agencies operating in economically less developed countries. This topic is vital to public policy makers, the IT industry, and IS practitioners. In this article, we provide a review and interpretation of technology transfer in the particular context of e-government initiatives and knowledge management. To our best knowledge, despite the need for a stronger theoretical foundation supporting e-government (Bekkers & Homburg, 2007; Jaeger & Thompson, 2003), the knowledge management literature has been largely overlooked in existing e-government research addressing technology transfer. Therefore, we draw upon the insights obtained through this review to extend the Information Technology Transfer Life-Cycle Model, proposed by Baark and Heeks (1999). This well-known model in the field of e-government is still considered a dominant lens for examining real-world projects that attempted to accomplish the transfer of e-government technology from developed to less developed countries. However, as pointed out by Baark and Heeks (1999), this model was fully derived from empirical observations of four e-government TT projects in China. To date, this model has been empirically tested by a number of scholars (Al-Mabrouk & Soar, 2008; Kaasbøll & Nhampossa, 2002; Kasimin, Ibrahim, & Yusoff, 2009; Kimaro & Nhampossa, 2004; Lwehabura & Matovelo, 1999). However, although the different tests concluded that the model is incomplete, these theory testing contributions were also empirical. As such, this article is aimed at improving the theoretical foundation of e-government research by improving understanding of the dynamics of the transfer of e-government technology through a review of popular TT models accrued from the field of knowledge management. Accordingly, this article will propose a heuristic frame that merges e-government and technology transfer issues from the perspective of recipient organizations. The remaining part of this article is organized as follows. In Section 2, the methodological procedure adopted in this article is unveiled. In

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Section 3, an overview of e-government is presented in order to contextualize and discuss the Information Technology Transfer Life-Cycle Model. Section 4 discusses the primary components of technology transfer, as well as a number of popular TT models. Subsequently, based on the discussion and interpretation of both e-government and technology transfer literature, a heuristic frame for e-government technology transfer is proposed in Section 5, as well as five propositions to be further tested. Finally, concluding remarks are set forth in Section 6. 2. Research method This is a theoretical essay aiming at developing a heuristic frame to describe the e-government technology transfer process from developed to developing countries. According to Winter (1998, pp. 172–3): “A heuristic frame corresponds to a degree of problem definition that occupies an intermediate position on the continuum between a long and indiscriminate list of things that might matter at one end and a fully formulated controltheoretic model of the problem at the other. Within a heuristic frame, there is room for a wide range of more specific formulations of the problem—but there is also enough structure provided by the frame itself to guide and focus discussion. On the other hand, a rich variety of different heuristic frames may represent plausible approaches to a given problem”. Thus, by analyzing technology transfer models, we developed a theoretical triangulation (Patton, 1990; Scandura & Williams, 2000; Yin, 1994) in order to propose a heuristic frame accrued from the critical review of the analyzed models. More specifically, we have searched for highly cited models of technology transfer that were developed based on empirical evidence concerning the transfer of technology from developed to developing countries. Thus, our search for relevant theoretical models was motivated by our need to improve representation of the structure of technology transfer projects involving e-government systems. Consequently, the first step in this study was identifying articles relevant to our research objective. The initial search was carried out within the environment provided by two of the primary scientific platforms of social sciences: Web of Science and Emerald Insight. The period of interest was between 1990 and 2013, while the selected keywords were: technology transfer, knowledge management, developing country, information system and project. Beyond these data sources, manual searches were carried out focusing on the reference list of the first set of identified articles, as well as manual searches in the following journals addressing e-government: Electronic Journal of Information Systems in Developing Countries, Government Information Quarterly, European Journal of e-Practice, and the Electronic Journal of e-Government. In this way, this initial effort resulted in a set of 132 articles. Subsequently, the following step consisted of selecting articles proposing a model of technology transfer between developed and developing countries. By searching the body of the selected studies, it was possible to select five studies containing models of technology transfer and from them to develop a heuristic frame for e-government technology transfer between developed and developing countries. 3. Conceptual foundation of e-government technology transfer The direct benefits associated with e-government initiatives are widely known. For instance, e-government is concerned with creating better service delivery to citizens and businesses (Millard, 2008) and it is believed to be the driving force behind the modernization of public administration (Bekkers & Homburg, 2007). However, the discussion regarding the proposition of a model for e-government TT requires a deeper conceptual grounding for understanding e-government concepts, as well as possibilities for technology transfer in this area (Dawes, Gharawi, & Burke, 2012). Hence, this section discusses the mainstream concepts and challenges associated with this phenomenon

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in order to contextualize the discussion around the Information Technology Transfer Life-Cycle Model (ITTLCM) proposed by Baark and Heeks (1999). 3.1. Defining e-government applications From a techno-centric perspective, e-government applications can be deemed as isolated technical artifacts that are comprised of a combination of hardware and software (Heeks, 2002). However, this perspective is certainly too restrictive, as the implementation of an e-government application also affects the social context. According to Avgerou and Walsham (2000), the relationship between technology and social context is bi-directional, as the social context of the deployment also impacts the technology during deployment. The Organization for Economic Co-operation and Development (OECD) defines e-government as: “The use of information and communication technologies, and particularly the internet, as a tool to achieve better government (p. 23)” (OECD, 2003). Essentially, e-government addresses the relationship (transaction) between the public administration and the citizen (customer) (Bellamy & Taylor, 1994). This concept involves more than getting a wide range of services online. According to Bekkers and Homburg (2007), e-government concerns the transformation of government administration, information provision and service delivery by the application of new technologies, delivering government services. Conversely, Heeks (2006) cites e-government as an instance of how government can improve the efficiency of its services by creating a citizen-centered electronic presence and undertaking the redesign of internal activities. Stanforth (2006), in turn, considers e-government application as a socio-technical arena within which information and communication technologies (ICTs) are being applied to organize public management in order to increase efficiency, transparency, accessibility and responsiveness to citizens. Another important aspect of e-government refers to the boundaries between the state and the market. Margetts (2006) indicates that the implementation of e-government applications contributes to the creation of an electronic, minimal state, more transparent, agile and accountable. Despite these generic definitions of e-government, the concept is continually subject to change. Heeks (2006) claims that, over the past decades, e-government initiatives involved two stages. The first stage addressed information dissemination phase in which governments cataloged information for public use. The second phase concerned transaction-based e-government (e-service delivery). Examples of the first phase include online forms for social security benefit applications and patent applications, while examples of the second phase include paying taxes online and integrating government data. Beyond the two traditional stages, a third stage is rapidly gaining importance. This new third stage consists of e-feedback and e-participation (Lenova, 2009; Osmo, 2008). 3.2. Challenges for e-government applications Despite the promising expectations, e-government still faces a lot of serious challenges as it continues to develop (Jaeger & Thompson, 2003; Verdegem & Verleye, 2009). Typically, e-government initiatives are associated with the deployment of a complex ICT infrastructure (Stanforth, 2006). This complexity stems largely from the need to integrate new e-government initiatives with the legacy systems and applications. This results in old and new platforms coexisting and new ones being implemented all the time (Ciborra, 2003). Implementing e-government is also considered a challenging process as it requires substantial organizational and technical change (Beynon-Davies & Williams, 2003). Beyond the technical challenge, the fit of ICT to the cultural environment in which e-government initiative is being enacted is also an important issue (Ciborra, 2003; Heeks, 2004). Avgerou and Walsham

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(2000) argue that the design and implementation of e-government initiatives in developing countries must be able to address the specific contextual characteristics of the organization, sector, country, and region. 3.3. e-Government in developing countries As stated by Heeks (2002), e-government has already arrived in developing countries, though it is essentially an imported concept based on imported designs. In practice, the diffusion is not globally-even. Heeks (2004) points out that a small number of rich countries such as the US, UK, Canada, and Singapore are seen as the vanguard of e-government applications, while poor countries in Africa experience fragmented e-government implementation with limited support for government agencies, especially when it comes to government operations in rural areas. In fact, the gap between developed and developing countries is enormous. Stanforth (2006) has maintained that successful e-government initiatives in developing country contexts are still rare. This is because successful implementation of adoptable e-government initiatives in that context requires complex customization between technology and implementation context in developing countries (Heeks, 2006). In fact, according to Heeks (2002), it is common for e-government initiatives in developing countries, despite of much effort with planning, analysis and design work, that the designed information system never became operational, or the whole initiative simply collapses shortly after implementation. Nevertheless, in spite of the limited practical results until now, practitioners, academics, international agencies, and policy makers are increasingly recognizing the potential of e-government initiatives to support processes of socio-economic growth in developing countries. The need for effective diffusion of e-government applications as a potentially fundamental apparatus to support development and economic growth is also widely supported by researchers (Heeks, 2006; Millard, 2008; Osmo, 2008). Given the potential impact of e-government, adoption is also considered an important aspect for the success of e-government initiatives in developing countries, as high adoption of these initiatives increases the chance that e-government will enable social and economic benefits to citizens (Margetts, 2006). 3.4. e-Government technology transfer The transfer of e-government technology is considered a complex endeavor. Many attempts to transfer IT from the Western World to developing countries have been carried out, and many failures have been reported due to lack of consideration for the context of the computer system (Baark & Heeks, 1999). Heeks (2002) points out that the most extreme form of technical cooperation between government agencies of different countries occurs when developed country designers create an e-government application for a developed country context and this application is subsequently transferred to a developing country. In such situations, the actuality of local conditions in the developing country might not have been considered at all in the original application design, and a considerable design–actuality gap is therefore likely, which leads to a significant risk of IS failure. Although a number of authors employ the term information technology transfer, instead of e-government technology transfer, we have opted for the latter term (e-government technology transfer). This perspective sees e-government as public information systems after the advent of internet (Heeks, 2004; Stanforth, 2006). For the sake of consistency, we only use e-government technology transfer to refer to the process of adapting an e-government application from the context of a donor to the context of a recipient organization. While Heeks (2004) considers e-government to be a global project of technology transfer, itself, whereby designs from one context are transferred into a different context, different authors report on the execution of projects that involves collaboration between government agencies of different countries to transfer e-government technology.

For example, Nhampossa (2005) describes the transfer of a European Health information system to Mozambique. He found that significant adaptation was required to ensure that the system fits well with the cultural context of the implementation environment. Another example of a complex e-government TT program was provided by Stanforth (2006). She reports the transfer of e-government systems to the Ministry of Finance of Sri Lanka. Kifle, Mbarika, and Tan (2007) examined the factors influencing the transfer of telemedicine technology to SubSaharan Africa, where telemedicine is described as the use of information technologies to exchange health information and provide health care services across geographical, social and cultural barriers. Ibrahim and Kasimin (2010) examined the structure of TT projects that involve the transfer of e-government applications from developed countries to Malaysia. In short, designing citizen adoptable e-government initiatives is still a challenge to government agencies of many developing countries. Consequently, transferring e-government methodologies from advanced public organizations at developed countries to public organizations at developing countries has become both a critical necessity and a significant challenge for project managers of e-government initiatives. Thus, the amount, scope, and complexity of such projects are rapidly growing. 3.5. Information Technology Transfer Life-Cycle Model In response to the need to improve understanding on the growing phenomenon of e-government technology transfer, Baark and Heeks (1999) provide a very simple TT model addressing the transfer of e-government or information technology associated with donorfunded projects. This well-known model was developed based on fieldwork evidence from four TT projects in China, which involved the receipt of e-government applications from developed countries. According to the Information Technology Transfer Life-Cycle Model (ITTLCM), the transfer can be conceived as five processes, as illustrated in Fig. 1. Essentially, the model describes technology transfer as a cycle. The cycle starts out with “Choice of Technology”, which is often completed prior to project funding. “Purchase and Installation” refers to the procurement and the training needed to install the software and hardware. The objective of “Assimilation and Use” is to create the conditions for the user to develop the necessary competences to use the system for various purposes and maintain it. “Adaptation” regards the need to change the system so that it fits the local needs better. In the final phase, which is called “Diffusion/Innovation”, the recipient organization that has gone through a learning process to adopt the system can undertake diffusion to other organizations and innovate the system.

Choice of Technology

Purchase and Installation

Diffusion/ Innovation

Adaptation

Assimilation and Use

Fig. 1. The Information Technology Transfer Life-Cycle (ITTLCM). Source: Baark and Heeks (1999).

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Baark and Heeks (1999) consider two types of technology to be transferred. The first type concerns general development projects and regards a means to achieve managerial objectives. The second type refers to ICT-specific projects that are aimed at raising the technological capabilities of the recipient organization. According to this model, installation, assimilation and adaptation are highly dependent on the kind of technology to be implemented. Although this model was not previously compared with existing technology transfer literature, empirical testing has been intense, as diverse scholars have attempted to further validate the model. However, different authors have only attempted to test the model by carrying out case studies. For instance, Kaasbøll and Nhampossa (2002) have applied the model to analyze the transfer of an e-government application in the health sector. In this project, South Africa was the donor country, while Mozambique was the recipient country. Lwehabura and Matovelo (1999) applied the model to improve understanding of TT projects addressing information services of libraries in African universities. Additionally, Kimaro and Nhampossa (2004) have employed elements of the model to guide the case study on the sustainability of donor funded TT projects in Mozambique and Tanzania.

4. Technology transfer from knowledge management perspective Given the growing need to accomplish consistent service improvements, organizations started to realize that relying solely on in-house experience is not sufficient (Choo & Johnston, 2004). Here, we approach technology transfer from a knowledge management perspective. In this way, we adopt a technology perspective accrued from Frey (1987), which points out that technology can be an object, a process, or knowledge that can be created by human intention. Accordingly, technology tends to be the integration of these three components. This section provides an overview of the most important issues associated with TT programs and discusses five popular qualitative TT models through a literature review. The purpose of this review is to include knowledge accumulation and creation into the e-government discipline by summarizing what the knowledge management discipline knows about technology transfer.

4.1. Technology as a commodity As a starting point, we make a distinction among data, information and knowledge. There seems to be a consensus in literature defining data as raw numbers and facts, information as processed data, and knowledge as authenticated information (Machlup, 1980; Vance, 1997). Nonaka (1994) states that knowledge can also be defined as a justified belief that increases an entity's capacity for effective action, while Vance (1997) defines knowledge as the capacity to use information, which is acquired through learning and experience. Furthermore, Nonaka (1994) distinguishes two basic dimensions of knowledge: tacit and explicit. The tacit dimension is comprised of both cognitive and technical elements. The cognitive element refers to mental maps, beliefs, paradigms, and view-points and the technical element consists of concrete know-how, crafts, and skills. Conversely, the explicit dimension is articulated, codified and communicated in symbolic form or natural language. Examples of explicit knowledge include manuals and process descriptions. Basically, knowledge, which in the view of Frey (1987) can also be regarded as technology, is increasingly seen as a type of commodity, as it can be created, stored, retrieved, transferred and applied. In practice, knowledge can be embedded into and carried through multiple entities including organization culture and identity, routines, policies, systems, and documents (Duane & Lee, 2000). Moreover, knowledge or intellectual assets comprise patents, trademarks, copyrights, and know-how (Spender, 1996).

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4.2. Technology transfer concepts After examining how knowledge is progressively turning into the most valuable commodity, we can concentrate on the most important characteristics of a technology transfer process. First of all, several scholars claim that the relationship between donor and recipient organizations in a TT project goes way beyond selling and buying equipment (Darr & Kurtzberg, 2000; Gupta & Govindarajan, 2000; Jagoda, 2007; Vance & Eynon, 1998). International TT programs are viewed as learning interactions and decisions are based upon the tacit-explicit characteristics of the knowledge to be transferred. TT programs involve various levels: transfer of knowledge between individuals, from individuals to explicit sources, from individuals to groups, between groups, across groups, and from the group to the organization (Gupta & Govindarajan, 2000). As such, technology transfer depends on how easily domainspecific knowledge can be transported, interpreted and absorbed (Vance & Eynon, 1998). According to Ramanathan (2001), the TT mode chosen depend on the corporate strategies of the donor and the recipient, as well as on the technological capability of the recipient. Conversely, Choo and Johnston (2004) also point out the need for an active role of the recipient. In importing knowledge from outside, the recipient ends up being both creative and proactive in acquiring knowledge about new technologies as well as knowledge about the market, thereby developing additional technological capabilities (Choo & Johnston, 2004). While organization to organization ‘learning’ is commonly used to describe interactions, it is the boundary role persons (BRPs) that are the true entities involved in the context of the transfer (Inkpen & Currall, 1997). Thus, learning takes place between BRPs through a knowledge interface. In effect, the majority of the literature focuses on technology transfer channels (Sung & Gibson, 2000). Technology transfer channels can be informal or formal, personal or impersonal (Holtham & Courtney, 1998). Additionally, another important factor influencing the complexity of TT programs includes the distance between knowledge donor and knowledge recipient, which influences the extent to which technology needs to be adapted (Dawes, Creswell, & Pardo, 2009; Dawes et al., 2012; O'Dell & Grayson, 1999; Simonin, 1999). Finally, important TT mechanisms include R&D partnerships, collaboration with universities and research institutes, and cooperation with small firms, as well as the use of explicit knowledge found in how-to manuals, lessons learned databases, patent documentation and best practice guides (Inkpen & Dinur, 1998). In this way, learning takes place at both the donor as well as the recipient side, as knowledge is constantly accumulated in the course of TT projects. 4.3. Common barriers to technology transfer Despite potential performance improvements generated by TT programs, it is well known, however, that the effectiveness of TT initiatives varies significantly among organizations (Irwin & Moore, 1991; Sung & Gibson, 2000). According to Jagoda (2007), technology transfer initiatives often fall far short of delivering on all the expected results. Moreover, Agarwal, Gupta, and Dayal (2007) determined that the effectiveness of technology transfer initiatives varies significantly among organizations. For instance, Pursell (1993) argues that many post-World War II technical efforts failed because donor countries ignored and misunderstood the cultural environments, assuming that all countries should follow the same patterns of industrialization. A fundamental reason for the difficulties in designing complex TT programs stems from the inherent complexity of IT systems and organizations. TT programs are affected by factors such as the characteristics of knowledge (Irwin & Moore, 1991), and technology exchange between donors and recipients (Gupta & Govindarajan, 2000; Sung & Gibson, 2000; Zhang & Dawes, 2006). Additionally, barriers to cross-border TT programs include control and oversight of project activities, incompatibilities among

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organizations, ill-structured projects, high costs of training, low flexibility and transparency (Akubue, 2002), technology (Agarwal et al., 2007), cultural, communication, and geographical distance between senders and recipients (Dawes et al., 2012; Sung & Gibson, 2000). Choi (2009) also claims that technical know-how is relatively immobile and it must be recreated by the recipient organization. As a result, the burden of developing technical know-how is subject to a high level of uncertainty and becomes, therefore, an important hurdle to technology transfer. Furthermore, transferring technology requires a broad set of resources and capabilities that are not controlled by a single actor (Simonin, 1999). This raises coordination issues, such as how collective actions of independent actors can be governed, thereby posing conflicting requirements such as autonomy versus control (Akubue, 2002). In practice, a crucial problem for managers of TT programs is how to choose control mechanisms, such as the autonomy level granted to the project participants, the specification of performance evaluation criterion for project activities, and the breadth of adopted communication mechanisms (Gupta & Govindarajan, 2000). 4.4. Examining existing technology transfer models Since the early 1970s, considering the complexities associated with technology transfer, researchers, consultants and practitioners have proposed TT models that could facilitate the effective planning and implementation of projects (Ramanathan, 2001). As such, the search for valid technology transfer models has resulted in several models throughout the past decades (Boisot, 1995; Gupta & Govindarajan, 2000; Holden, 2002; Nahapiet & Ghoshal, 1998; Nonaka, 1994; Nonaka & Takeuchi, 1995). In this section, we briefly examine some of the most popular models of technology transfer that have been developed over the years to help donors and recipients of technology understand the technology transfer process. The models selected here have in common the very fact that they consider TT projects as knowledge management systems (KMS) developed to transfer technologies from developed to developing countries. However, they were not designed with a particular focus on information technology artifacts. 4.4.1. Bar-Zakay model of technology transfer Although the Bar-Zakay model comprises a comprehensive set of constructs to explain a TT project, the model was developed in 1971, when these projects were less common and less complex than today. However, the model reveals important patterns detected in TT projects (Bar-Zakay, 1971). It divides the TT process into four sequential phases, which include Search, Adaptation, Implementation, and Maintenance. The model suggests a one-way direction of knowledge flowing from donor to the recipient organization. Beyond this, for each phase, the model describes activities that can be regarded as decision points with go or no-go outcomes. The Bar-Zakay model also represents different responsibilities for the donor and the recipient organization. As such, the upper side of the diagram is dedicated to the requirements of the donor, while the lower side concerns the recipient. In general, the model puts emphasis on the necessity for both donor and recipient to acquire skills throughout the entire TT process. The model indicates a project management character by suggesting sequential activities, such as technological forecasting, long-range-planning and gathering of project-related intelligence. It is valuable in highlighting the uncertain character of long-term planning. The Bar-Zakay model is depicted in Fig. 2. 4.4.2. The Proper TT model The “Proper” model describes a deliberated and planned process of technology transfer and indicates that the user of the technology plays a central role in the TT process. In practice, the user plays a crucial role in the adaptation of the technology. Hence, the characteristics of the user are crucial to the adaptation to local conditions.

Fig. 2. Bar-Zakay model of technology transfer. Source: Bar-Zakay (1971).

Another important aspect of this model refers to the conceptualization of a TT project as a circular and ongoing process, which leads to continuous learning and improvement. The Proper model implies that a new cycle starts right after a cycle has been completed. Additionally, although the model is shaped as a circle, the model also recognizes distinguishing roles for the donor and the recipient organization (Cutrell, 1990). Clearly, the right hand-side of the model illustrates responsibilities of the donor organization, while the left side of the diagram corresponds to the responsibilities of the recipient organization, as presented in Fig. 3. 4.4.3. Role shifting model of technology transfer The role shifting model of technology transfer represents the conditions that enable the transfer of technology. This model suggests that a high level of continuing education and training results in the accumulation of technological capability that enables a prominent role in helping a technology transfer (Choi, 2009; Lips, 2001). It is also pointed out that elaborate plans for collaboration between recipients and donors contribute to achieve successful technology transfer. As such, elaborate plans are represented as the ‘Sun’ that illuminates the technology transfer process. However, the detailed plans should take into account the occurrence of a number of unexpected events.

Fig. 3. The “Proper” technology transfer model. Source: Cutrell (1990).

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Fig. 4. Role shifting model of technology transfer. Source: Choi (2009).

As shown in Fig. 4, the shifting model emphasizes the education level of the population as a central factor for speeding up the process of absorbing new technologies and incorporating them into the production process of both private and public organizations. According to this model, the speed of the TT process is primarily determined by technical capacity to assimilate, adapt and develop new technology. Since the model considers people as the mainstream factor, the model also suggests the necessity to invest in the infrastructure to better education and training as a means of developing human capital. As a result of this focus on human capital, investment in education is seen as a ‘Fertilizer’ of the innovation process. This model for technology transfer results in competitive advantages for a country accruing from the development and diffusion of innovations, which are represented as an ‘Apple’. In this respect, the model is capable of explaining South Korea's successful transfer of technology for its national economic development, as this country transformed itself from an agrarian society to one of the world's most highly industrialized nations.

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4.4.4. Stage-Gate model for technology transfer The primary aim of the Stage-Gate model is to propose an integrated approach that might be used by recipients of technology in planning and managing TT projects. This detailed model provides the ability to study a TT process in terms of activities, milestones, and decisionpoint sequences. This stage-gate structure was proposed by Jagoda (2007) for developing a systematic approach for planning and managing TT projects for multinational enterprises with operations in developing countries. The model places an important role on the processes of defining the problem situation and identifying value enhancing technologies to tackle the problem. Consequently, the model highlights the importance of a precise definition of the problem situation as a means of supporting the decision-making process with respect to the technology. This model represents TT processes as a set of predetermined stages and gates. The stages are made up of prescribed tasks with crossfunctional and simultaneous activities. The gate or controlling point is at the entrance to each gate. Based on the information generated at each stage, in-depth and critical analysis is carried out at the gate that follows the stage. Based on the evaluation, a decision may be taken to go forward, kill the project, put it on hold, or recycle it. This approach was designed to enable proactive measures that can be taken to avoid or minimize problems throughout the execution of TT projects. The model is shown schematically in Fig. 5.

4.4.5. Transnational public sector knowledge networks (TPSKN) model Dawes et al. (2012) define TPSKN as transnational public sector knowledge networks aiming at sharing knowledge between government organizations in at least two different countries. These networks are formed for different reasons including the need to address a specific problem or the need to build certain kinds of capability among network members. The authors identified contextual elements that characterize the external and internal environments of the individual organizations that participate in TPSKNs, namely: information and knowledge context, organizational context, and national context. The first context addresses the main characteristics of the knowledge and information being exchanged (Zhang & Dawes, 2006); the organizational context embodies the structures, capabilities and constraints of the involved organizations (Eglene, Dawes, & Schneider, 2007); and the national context corresponds to cultures and political systems (Dawes & Prefontaine, 2003),

Fig. 5. Stage-Gate model for technology transfer. Source: Jagoda (2007).

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These contextual factors, as supported by Dawes et al. (2012), shape the way individuals perceive the world and the ways in which organizations work. They also create distances between the organizations involved in the knowledge sharing process, namely: cultural, political, intention, organizational, relational, knowledge, resource, physical, and technical distances. According to Dawes et al. (2012, p. S115–S117), the cultural distance addresses the gap of beliefs, values and practices between the organizations. The political distance reflects the gaps and conflicts among the existing laws and policies. The intention distance tackles the differences in mission and goals between the organizations. The organizational distance refers to the degree of dissimilarity between the partners' business practices, institutional heritage, and organizational culture. The relational distance is shaped by the duration and type of historical interactions among the participating organizations. The knowledge distance refers to differences in the existing knowledge bases of the participating organizations. The resource distance reflects differences in both the amount and type of resources participants need form or contribute to the network. Physical distance addresses the relative geographical location of the organizations involved in the network. Finally, the technical distance is shaped by the differences in the IT infrastructure and capabilities of the participating organizations. Based on that, Dawes et al. (2012) proposes a dynamic model for transnational knowledge networks, as depicted in Fig. 6. 4.5. Analysis of TT models Given our objective to propose a heuristic frame for e-government technology transfer, it is possible to identify shortcomings of the above-mentioned models in addressing TT projects. These differences are considered of limited importance, but it seems important to briefly discuss them. First of all, the models outlined in the previous section do not address specific characteristics of e-government applications. Besides, the models are also not focused on a particular technological domain. As such, they do not consider specific IS characteristics. In addition, the models do not consider the need to integrate a new technology. Although the models were not designed with a focus on e-government applications, it is possible to extract TT elements from the models that were not previously considered by Baark and Heeks (1999) in the ITTLCM approach. Despite these shortcomings, the selected models display a consistent set of communalities in their conceptualization of the TT process. All models share the same focus of e-government TT projects, which are aimed at transferring technology from developed to

developing countries. Hence, in general, the models take into consideration the differences of context between the donor and the recipient organization. The different conceptualizations also have a common ground in the sense that they view technology transfer primarily as a sequential process. As such, the positivist nature of the selected models does not highlight processes involved in institutionalizing the transfer that have a more social nature, such as politics, power, and culture of the potential adopters of the technology. The main findings from our review of the models are summarized in Table 1. 5. Towards a heuristic frame for e-government technology transfer In response to the pressing need for an e-government TT model for guiding professional practice, this section proposes an integrative meta-model that builds upon accumulated e-government and knowledge management experience from the models presented above. We propose a heuristic frame called E-government Transfer Model (ETM) that may be regarded as an improvement of the Information Technology Transfer Life-Cycle Model (ITTLCM) with greater emphasis in the process of technology transfer. The ETM model has been developed based on lessons learnt from the study of popular TT models of the knowledge management literature, so as to advance our understanding on the dynamics of technology transfer. In essence, ETM incorporates conceptual constructs listed in Table 1, which were derived from our review of the five existing TT models accrued from the field of knowledge management. The process of reviewing the models allowed us to improve our understanding of the elements of a technology transfer process and, consequently, it became quite clear that the model proposed by Baark and Heeks (1999) does not present a complete view of all relevant aspects of technology transfer. As a matter of fact, we have observed the necessity to incorporate additional elements into our new model that have a direct impact on the success of TT projects. As indicated by different scholars (Choi, 2009; Cutrell, 1990; Jagoda, 2007; Orlikowski & Hofman, 1997), the unexpected changes to plans, the technical capability of the recipient organization and the evaluation of the project are regarded as fundamental elements affecting the outcome of a TT project. As a consequence of this broader view, we propose that the execution of projects to transfer e-government technology is a circular process determined by: (1) Perception of Problem, (2) Choice of Technology, (3) Purchase and Installation, (4) Technological, Managerial & Organizational Capabilities of the Recipient, (5) Adaptation, (6) Adoption, (7) Diffusion and Innovation, (8) Feedback to Donor, and (9) Unplanned Elements.

Contextual Distances creates

Layers of Context National; Organizational; Informational

afffect

Participant in Country A

Cultural Political Organizational Relational Knowledge Resource Physical Technical

creates

Participant in Country B Layers of Context National; Organizational; Informational

Processes and Interactions

engage in

Knowledge & information sharing Collaboration Learning

generate Hard and soft products Fig. 6. TPSKN model (Dawes et al., 2012).

engage in

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Table 1 Summary of technology transfer models. Technology transfer model

Characteristics

Conceptual construct

Bar-Zakay model

– Search for technology – Sequential phases: Search, Adaptation, Implementation, Maintenance – Decision-points with go or no-go outcomes

Proper model

– User-driven adaptation – Circular model – Specific roles for donor and recipient

Role shifting model

– – – – – – – – – . . . – – –

– – – – – – – – – – –

Choice of Technology Purchase and Installation Adaptation Adoption Perception of Problem Adaptation Adoption Diffusion and Innovation Perception of Problem Technological Capability of the Recipient Diffusion and Innovation

– – – – –

Purchase and Installation Unplanned Events Adaptation Feedback to Donor Contextual Distances

Stage-Gate model

TPSKN model

Description of problems Training staff Innovation (Apple) as a result of human interaction Technology gap Identify value enhancing technologies Technology gap Unplanned events are expected Provision of feedback as a post-technology transfer activity Layers of context National Organizational Informational Contextual distances Trans-governmental model Dynamic and process-based

These major stages in the life cycle are shown schematically in Fig. 7. Besides, all the processes circumvent the Unplanned Elements stage, which is potentially linked to all other stages (dotted lines). In other words, the dotted lines between the Unplanned Elements stage and the others indicate that changes can occur throughout all the

technology transfer process. Additionally, our heuristic frame also implies that individuals of both donor and recipient organizations are involved in all aspects of the cycle, as the technology transfer can be seen as a collaborative effort that implicates shared responsibilities. Below, we provide a brief description of each element.

Fig. 7. E-government Transfer Model (ETM).

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5.1. Perception of Problem It is necessary to stress the importance of establishing the need for a technology transfer project. In this respect, the involvement of frontline staff from developing country, at this initial project stage, can be seen as an effective way of eliminating the conception–reality gap, as the front-line staff is capable of providing a rich description of the context of the operations of the government agency in a developing country (Choi, 2009). As suggested by Cutrell (1990), the perception of the problem is subject to constant improvements (refinement) as experience is accumulated by finishing cycles of TT projects. The sense of urgency for the project is determined by the perception of the problem situation. 5.2. Choice of Technology An important step in trying to address challenges in implementing e-government applications within a complex organizational setting is the available choice of technologies. Despite the long duration of a technology transfer project, the choice of technology takes place in an initial stage of the process (Bar-Zakay, 1971). In general, technology is selected based on limited information and has important implications for the project (Nhampossa, 2005). Consequently, an inadequate choice of technology is likely to generate significant risks for the TT project. 5.3. Purchase and Installation Since e-government applications are comprised by a combination of software and hardware, it is necessary to dedicate attention to the intellectual property rights associated with the software (Jagoda, 2007). Consequently, agreeing upon a basis for the valuation of technology right from the beginning to specify the agreements related to the intellectual property protection is extremely important to promote stability in the network formed by the participating actors. 5.4. Technological, Managerial & Organizational Capability of the Recipient The technological, managerial and organizational capabilities of the recipient are critical factors in dealing with the complexity of information systems associated with e-government applications. The lack of human resources, in combination with inadequate skill levels and pressures of everyday work intensify the complexity of implementing public information systems in a developing country context (Joia, 2005). However, influencing the technological, managerial and organizational capabilities of the receiver involves monitoring user characteristics and behavior and, therefore, needs a profound knowledge base on factors, such as: user needs, ICT literacy levels, satisfaction of e-services, and impact of online public services (Choi, 2009). Beyond the user, it is also necessary to develop capabilities of employees of the government agency of a developing country (Joia, 2005). In this respect, new work processes need to be accompanied by deep training programs, structured communications strategies and job redesign (Stanforth, 2006). 5.5. Adaptation The implementation of a foreign design of an e-government application requires complex customization between technology and implementation context in developing countries (Bar-Zakay, 1971). Due to different implementation contexts, it is necessary to identify and apply changes to an e-government application in order to enable it to fulfill user's requirements that are different than the original ones (Cutrell, 1990). In fact, our model recognizes that there are situation-specific factors for each information system that will determine success and failure, and hence the system needs to be adapted in order to fulfill the new user's requirements. According to Nhampossa (2005), the systems do not always match the needs, organizational structures and the way work is carried out in the developing countries, and the scarcity of

resources and competences of developing countries makes the adaptation of computer systems necessary, but also very difficult. In essence, adaptation can be seen as the process of adapting a technology for a different use other than its original one or for a different application. In addition to the technical changes, the implementation of a new e-government application also requires organizational changes that may accompany further deployment of ICT at the level of the organization. Organizational characteristics include organizational culture, resources and rewards. 5.6. Adoption Adoption plays an important role in the success of e-government initiatives. Low adoption, particularly by citizens, indicates inadequate utilization and rejection of the initiatives by the intended users. This may lead to failure of e-government initiatives. This is particularly important in the context of developing countries where e-government is a newly imported innovation. Adoption is an important aspect for the success of technology transfer programs from developed to developing countries (Jagoda, 2007). Here it is important to mention that the model proposed by Baark and Heeks (1999) employed the term “Assimilation and Use” to refer to the process of adoption of a new technology. High adoption of the initiatives increases the chance that e-government will facilitate social and economic benefits to citizens (Margetts, 2006). It is therefore imperative to understand and proactively consider issues underlying citizen adoption of e-government applications in that context. Jagoda (2007) argues that factors influencing adoption include adequate awareness, supportive mindset, and reliable infrastructure. 5.7. Diffusion and Innovation As citizens and businesses are demanding faster delivery of public services, the ability to generate new solutions and implement them is becoming increasingly valuable (Margetts, 2006). Since e-government involves the deployment of a complex ICT infrastructure, it faces a number of risks in relation to implementation, project management and policy, that have to be appropriately managed (Margetts, 2006; Nhampossa, 2005). According to Rogers (1983), diffusion is the process by which a communication is communicated through certain channels over time among members of a social system. Accordingly, the innovative solutions that were generated during the adaptation of the system can be considered innovations that need to be properly communicated to the users of the technology. 5.8. Feedback to Donor Given the experience acquired with the implementation, project participants can provide feedback on both the execution of the TT project, as well as the adoption of the e-government application in the developing country context (Jagoda, 2007). For instance, it is possible to provide feedback on how users have chosen their preferred channel to interact with the government, or change their preferred channel depending on their needs and circumstances while interacting with the government (Margetts, 2006). The process of providing feedback can be deemed as an evaluation of the project, which contributes directly to “refine” the perception of the problem experienced by the government agency of a developing country. 5.9. Contextual Distances As supported by Dawes et al. (2012) in the TPSKN model, there are nine contextual distances that must be overcome in order to succeed in transferring knowledge between the donor organization and the recipient one. As these distances unfold over time, the processes and interactions exert their own influence on these distances serving to

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narrow or widen them depending on the actors' options, choices, and situations. The results of these dynamics are both hard and soft products. The former include formal decisions, laws, systems and data resources, while the latter refer to trust, power and informal relationships between the participant organizations.

broad range of relevant literature. Accordingly, this review tries to be unique among other reviews of the e-government literature, via its application of knowledge management models to analyze a relevant but under-researched area. As such, five major points emerge from the above discussion:

5.10. Unplanned Elements

1. While there are many possible structures for good theory about the transfer of e-government technology, only a few of these structures can be seen in current theorizing. Increased awareness of the options, open discussion of their advantages and disadvantages, and explicit characterization of future theoretical statements in terms of dimensions and categories suggested here should promote the development of better theory. As such, the discussion on technology transfer presents a rich conceptual grounding for understanding how government agencies of different countries cooperate to improve the technological capabilities (Kaasbøll & Nhampossa, 2002; Kasimin et al., 2009). By developing the ETM model, it also became clear that the knowledge management literature on technology transfer provided a consistent conceptual grounding for further extending and refining the Information Technology Transfer LifeCycle Model. Despite the previous attempts to empirically validate ITTLC, this first attempt to rely on a literature review of other qualitative TT models has enabled the incorporation of new concepts that were previously overlooked such as Perception of Problem, Technological Capability of the Recipient, Feedback to Donor, Contextual Distances, and Unplanned Elements. 2. Our literature review revealed the many complexities associated with the execution of international e-government TT programs. Essentially, TT is not a monolithic but a dynamic and continuous organizational phenomenon, as it involves a complex socio-technical system characterized by the intensive interaction among several individuals with varying technical and communication capabilities (Akubue, 2002). In practice, e-government TT involves learning on both the side of the donor and the recipient organization (Nhampossa, 2005). Therefore, this process should not be regarded as a one-way traffic from developed to developing country, but rather as a collaborative and context-specific process based on mutual understanding about an information system and the different implementation contexts. 3. Our review also demonstrates the need for an integrative model to facilitate knowledge sharing between participants of international TT programs (Ibrahim & Kasimin, 2010; Stanforth, 2006). Such conceptual model must also provide structures that can allow research on the transfer of e-government knowledge to become more holistic, as well as a framework that can be understood by both policy makers and IS practitioners. It is envisaged that the proposed heuristic frame, after being validated and tested, might help in addressing many of the common problems that are currently faced by both donor and recipient organizations. 4. We have found theoretical evidence indicating that e-government solutions developed to fulfill specific requirements of the government agency of one country cannot simply be transplanted to other countries (Millard, 2008; Nhampossa, 2005). The implementation of externally developed information systems requires adaptations of the systems to the local organizational structure, routine and tasks (Jaeger & Thompson, 2003). However, we must be aware that developing countries are not the same (Heeks, 2006). There are significant cultural, historical and geopolitical differences that lead to contextual distances (Dawes et al., 2012) and influence the type and extent of adaptation that needs to be carried out. 5. The invisible aspects of e-government technology, such as knowledge, skills and organization, might be much more critical than the physical aspects, such as hardware, for the successful transfer of technology (Darr & Kurtzberg, 2000; Kasimin et al., 2009). Therefore, it is imperative for managers of technology to gain good insights into the donor environment, recipient environment, and the greater environment when planning and implementing an e-government TT project.

As argued by Gibson and Smilor (1991), technology transfer is often a chaotic, disorderly process involving groups and individuals who are likely to hold different views about the value and potential use of technology. Transferred technology is then more the result of an unplanned mixture of participants, solutions looking for problems, choice opportunities, and problems looking for solutions (Jagoda, 2007). Both problems looking for solutions (technology pull), as well as solutions looking for problems (technology push), are encountered. In this way, the model is also of value in explaining local IS improvisations in developing countries (Orlikowski & Hofman, 1997). In essence, ETM portrays technology transfer relative to e-government initiatives as resulting from the interaction among actors of government bodies of different countries (see Fig. 7). More precisely, as opposed to existing TT models examined in Section 4, ETM emphasizes the collaborative character of technology transfer by stipulating shared responsibilities for both the donor and the recipient organizations. ETM also emphasizes the importance of an intangible intellectual property resources. A second common assumption of ETM pertains to the nature of communication between representatives of source and recipient organizations. Our focus in this model is on public services, and how knowledge is created, used and becomes enacted within government organizations in the context of e-government technology transfer. In particular, we posit ICT to be both the product of human action as well as a medium for human action (Orlikowski & Hofman, 1997). A methodological contribution of ETM is the development of a set of constructs to assess the deployment of international TT programs. As such, the model provides a basis for structuring our review of accumulated knowledge, for identifying gaps in knowledge, and for developing propositions to guide future research. While the TT process is not instantaneous and collaboration occurs over time, the model explicitly take into account the learning experience of both the donor and the recipient. Using the integrative ETM model as a lens through which to examine projects involving the transfer of e-government technology, we formulated five research propositions to be further tested: P1. Proper alignment of the characteristics of the selected technology and the nature of the public service problems in a specific public sector environment associated with the recipient organization will facilitate e-government adoption. P2. It is feasible to significantly influence the technological capability of the recipient organization by means of technical training and supervision. P3. During the execution of an e-government technology transfer project, learning takes place at both the recipient and the donor organizations. P4. The greater the social contact between individuals employed by the donor and the recipient organizations, the faster the adaptation process of the e-government application takes place. P5. The more complete the feedback provided by the recipient organization, the faster becomes the implementation of new modules of the e-government application. 6. Conclusions In this article, we have presented a discussion of e-government technology transfer and based on a review, interpretation, and synthesis of a

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Finally, our analysis provides a theoretical blueprint to guide future research and facilitate knowledge accumulation and creation concerning the execution of e-government technology transfer projects. However, the heuristic frame proposed must be tested via its application in actual e-government technology transfer projects. Thus, our future steps include further elaboration and validation of the developed model.

References Agarwal, S. P., Gupta, A., & Dayal, R. (2007). Technology transfer perspectives in globalising India. Journal of Technology Transfer, 32(4), 397–423. Akubue, A. I. (2002). Technology transfer: A third world perspective. The Journal of Technology Studies, 28(1), 14–21. Al-Mabrouk, K., & Soar, J. (2008). Building a framework for understanding and improving information technology transfer process in the Arab countries. Proceedings of the 9th IBIMA conference on information management in modern organizations, Marrakech, Morocco. Avgerou, C., & Walsham, G. (2000). Information technology in context: Studies from the perspective of developing countries. Aldershot, UK: Ashgate Publishing. Baark, E., & Heeks, R. (1999). Donor-funded information technology transfer projects: Evaluating the life-cycle approach in four Chinese science and technology projects. Information Technology for Development, 8, 185–197. Bar-Zakay, S. N. (1971). A technology transfer model. Technological Forecasting and Social Change, 2, 321–337. Bekkers, V., & Homburg, V. (2007). The myths of egovernment: Looking beyond the assumptions of a new and better government. The Information Society, 23(5), 373–382. Bellamy, C., & Taylor, A. (1994). Exploiting IT in public administration: Towards the information policy? Public Administration, 72, 1–12. Beynon-Davies, P., & Williams, M.D. (2003). Evaluating electronic local government in the UK. Journal of Information Technology, 18(2), 137–149. Boisot, M. H. (1995). Information space: A framework for learning in organisations, institutions, and culture (London). Choi, H. J. (2009). Technology transfer issues and a new technology transfer model. Journal of Technology Studies, 35(1), 49–57. Choo, C. W., & Johnston, R. (2004). Innovation in the knowing organization: A case study of an e-commerce initiative. Journal of Knowledge Management, 8(6), 77–92. Ciborra, C. (2003). Unveiling e-government and development: governing at a distance in the new war. Department of Information Systems, Working Paper Series. London: London School of Economics and Political Science. Cutrell, J.D. (1990). Technology Transfer Systems for the Coming Decade. Washington, DC: Federal Highway Administration, Department of Transportation. Darr, E. D., & Kurtzberg, T. R. (2000). An investigation of partner similarity dimensions on technology transfer. Organizational Behavior and Human Decision Processes, 82, 28–44. Dawes, S. S., Creswell, A., & Pardo, T. (2009). Fron “need to know”to “nneed to share”: Tangled problems, information boundaries, and the building of public sector knowledge networks. Public Administration Review, 69(3), 392–402. Dawes, S. S., Gharawi, M.A., & Burke, B. (2012). Transnational public sector knowledge networks: Knowledge and information sharing in a multi-dimensional context. Government Information Quarterly, 29, S112–S120. Dawes, S. S., & Prefontaine, L. (2003). Understanding new models of collaborating for delivering government services. Communications of the ACM, 46(1), 40–42. Duane, A.R., & Lee, H. U. (2000). Technological learning management, firm growth and performance: An introductory essay. Journal of Engineering and Technology Management, 17, 231–246. Eglene, O., Dawes, S. S., & Schneider, C. A. (2007). Authority and leadership patterns in public sector knowledge networks. The American Review of Public Administration, 37(1), 91–113. Frey, R. E. (1987). Is there a philosophy of technology? 74th Mississippi Valley industrial teacher education conference, Chicago, IL. Gibson, D.V., & Smilor, W. (1991). Key variables in technology transfer: A field-study based on empirical analysis. Journal of Engineering and Technology Management, 8, 287–312. Gupta, A. K., & Govindarajan, V. (2000). Knowledge flows within multinational corporations. Strategic Management Journal, 21(4), 473–496. Heeks, R. (2002). eGovernment in Africa: Promise and practice. Paper No. 13. Institute for Development Policy and Management. Heeks, R. (2004). eGovernment as a carrier of context. Paper No. 15. Institute for Development Policy and Management. Heeks, R. (2006). Implementing and managing egovernment: An international text. London: SAGE Publications. Holden, N. J. (2002). Cross-cultural management: A knowledge management perspective. Harlow: Pearson Education Limited. Holtham, C., & Courtney, N. (1998). The executive learning ladder: A knowledge creation process grounded in the strategic information systems domain. In E. Hoadley, & I. Benbasat (Eds.), Proceedings of the Fourth Americas Conference on Information Systems. Baltimore, MD: New Lexington Press. Ibrahim, H., & Kasimin, H. (2010). Structuring a successful information technology transfer process: A case study from the perspectives of actor network theory. Journal of Information and Communication Technology, 9, 17–39. Inkpen, A.C., & Currall, S.C. (1997). International joint venture trust: An empirical investigation. In Paul Beamish, & J. Peter Killing (Eds.), Cooperative strategies: North American perspectives (pp. 308–334). San Francisco: The New Lexington Press.

Inkpen, A.C., & Dinur, A. (1998). The transfer and management of knowledge in the multinational corporation: Considering context. Working paper no. 1998-16. Pittsburgh, PA: Carnegie Bosch Institute. Irwin, H., & Moore, E. (1991). Technology transfer and communication: Lesson from silicon valley, Route 128, Carolina's Research Triangle and Hi-tech Texas. Journal of Information Science, 17, 273–280. Jaeger, P. T., & Thompson, K. M. (2003). Egovernment around the world: Lessons, challenges, and future directions. Government Information Quarterly, 20(4), 389–394. Jagoda, K. I. (2007). A stage-gate model for planning and implementing international technology transfer. (Doctoral Dissertation). Australia: University of Western Sydney. Joia, L. A. (2005). A framework for developing regional e-government capacity-building networks. Information Technology and International Development, 2(4), 61–73. Kaasbøll, J., & Nhampossa, J. L. (2002). Transfer of public sector information systems between developing countries: South-south cooperation. Proceedings of Social Implications of Computers in Developing Countries, Bangalore, India. Kasimin, H., Ibrahim, H., & Yusoff, M. B. (2009). Diagnosing an ongoing information technology transfer process: An approach based on ANT. International Journal of Networking and Virtual Organisations, 6(1), 47–63. Kifle, M., Mbarika, V., & Tan, J. (2007). Telemedicine transfer in sub-Saharan Africa: Investigating infrastructure and culture. Proceedings of the 9th international conference on social implications of computers in developing countries, São Paulo, Brazil. Kimaro, H. C., & Nhampossa, J. L. (2004). The challenges of sustainability of health information systems in developing countries: Comparative case studies of Mozambique and Tanzania. ECIS, 2004, 937–950. Lenova, M. (2009). New index for measuring feedback and e-participation effectiveness of egovernment in Russia. Proceedings of the 9th European conference on eGovernment (pp. 445–451). Lips, M. (2001). Designing electronic government around the world. In J. E. J. Prins (Ed.), Designing e-government (pp. 75–89). Kluwer Law International. Lwehabura, J. F., & Matovelo, D. S. (1999). Convergence of libraries and computer centres for African universities: A strategy for enhancing information services. Information Technology for Development, 8(4), 221–229. Machlup, F. (1980). Knowledge: Its creation, distribution and economic significance, Vol. I, Princeton, NJ: Princeton University Press. Margetts, H. (2006). Egovernment in Britain: A decade on. Parliamentary Affairs, 59(2), 250–265. Millard, J. (2008). Egovernment measurement for policy makers. European Journal of ePractice, 4, 19–32. Nahapiet, J., & Ghoshal, S. (1998). Social capital, intellectual capital and the organizational advantage. Academy of Management Review, 23(2), 242–266. Nhampossa, J. L. (2005). Re-thinking technology transfer as technology translation: a case study of health information systems in Mozambique (PhD dissertation). Department of Informatics. Oslo, University of Oslo. Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organisation Science, 5, 14–37. Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company: How Japanese companies create the dynamics of innovation. New York: Oxford University Press. O'Dell, C., & Grayson, C. J. (1999). Technology transfer: Discover your value proposition. Strategy and Leadership, 27(2), 10–16. OECD (2003). OECD E-Government Flagship Report “The E-Government Imperative”, Public Management Committee, Paris: OECD. Ongaro, E. (2004). Process management in the public sector: The experience of one-stop shops in Italy. International Journal of Public Sector Management, 17(1), 81–107. Orlikowski, W. J., & Hofman, J.D. (1997). An improvisional model for change management: The case of groupware technologies. Sloan Management Review, 11–21. Osmo, D. (2008). Benchmarking government in the web 2.0 era: What to measure, and how. European Journal of e-Practice, Vol. 4. Patton, M. Q. (1990). Qualitative evaluation and research methods (2nd ed.). Newbury Park: Sage. Pursell, C. (1993). The rise and fall of the appropriate technology movement in the United States, 1965–1985. Technology and Culture, 34(3), 629–637. Ramanathan, K. (2001). E-strategies for technological capability development. Proceedings of the Portland international conference on management and technology, July 29– August 2, Portland, US. Rogers, E. M. (1983). Diffusion of innovations. New York: Free Press. Scandura, T., & Williams, E. (2000). Research methodology in management: Current practices, trends, and implications for future research. The Academy of Management Journal, 43(6), 1248–1264. Simonin, B. (1999). Ambiguity and process of technology transfer alliances. Strategic Journal, 20, 595–623. Spender, J. C. (1996). Making knowledge the basis of a dynamic theory of the firm. Strategic Management Journal, 17, 45–62. Stanforth, C. (2006). Using actor-network theory to analyze e-government implementation in developing countries. Information Technologies and International Development, 3(3), 35–60. Sung, T. K., & Gibson, D.V. (2000). Knowledge and technology transfer: Key factors and levels. Proceeding of 4th international conference on technology policy and innovation (pp. 441–449). Vance, D.M. (1997). Information, knowledge and wisdom: The epistemic hierarchy and computer-based information system. In B. Perkings, & I. Vessey (Eds.), Proceedings of the Third Americas conference on information systems (Indianapolis). Vance, D.M., & Eynon, J. (1998). On the requirements of knowledge-transfer using IS: A schema whereby such transfer is enhanced. In E. Hoadley, I. Benbasat, E. Hoadley, & I. Benbasat (Eds.), Proceedings of the Fourth Americas Conference on Information Systems. Baltimore, MD: Association for Information Systems.

G.M.C. Cavalheiro, L.A. Joia / Government Information Quarterly 31 (2014) 195–207 Verdegem, P., & Verleye, G. (2009). User-centered egovernment in practice: A comprehensive model for measuring user satisfaction. Government Information Quarterly, 26(3), 487–497. Winter, S. (1998). Knowledge and competence as strategic assets. In D. Klein (Ed.), The strategic management of intellectual capital (pp. 165–187). Butterworth-Heinemann.

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Yin, R. (1994). Case study research: Design and methods. Sage Publications, 2nd. ed. Thousand Oaks: California. Zhang, J., & Dawes, S. S. (2006). Expectations and perceptions of benefits, barriers, and success in public sector knowlefge networks. Public Performance & Management Review, 29(4), 433–466.