The concept of the mobile wallet is increasingly adopted in developed and developing countries for improving the scale, productivity, and excellence of banking services. Oman is one of the most growing countries of the Middle Eastern economies. Acceptance of mobile wallets in Oman is being hindered by various inhibitors. There is no study in the Middle Eastern countries that addressed the concerns of probable inhibitors influencing mobile wallet acceptance from expert's perspective. In this study, eleven key inhibitors to mobile wallet adoption are identified from the literature and expert's feedback. This study employed Interpretive Structural Modelling (ISM) in conjunction with fuzzy MICMAC to reveal the intricate relationship among inhibitors to mobile wallet acceptance. To the end, an integrated hierarchical model is developed to understand the influence of a particular inhibitor on others. 'Anxiety towards new technology', 'Lack of new technology skills', 'Lack of awareness of mobile wallet benefits' and 'Complexity of new technology' have been reported as key inhibitors to promote mobile wallets in Oman. This study also suggests several recommendations for banking organizations and policymakers in developing the effective model to popularize mobile wallets in Oman.

Despite increased research interest on knowledge transfer in information systems (IS) outsourcing, the field still lacks sound and holistic understanding of the key factors influencing knowledge transfer success. The present paper attempts to provide a synthesis of existing theoretical perspectives and empirical findings related to the factors that facilitate or hamper knowledge transfer success in IS outsourcing. The data collection method is discussed and the key findings are presented. Conclusion is drawn and further research is suggested. Keywords: knowledge transfer, Information systems (IS) outsourcing BACKGROUNDIn today's knowledge-based economy, one of the major sources of competitive advantage has been the ability of the firm to transfer external knowledge efficiently and effectively (Argote and Ingram, 2000;Pawlowski and Robey, 2004;Sambamurthy and Subramani, 2005;Pérez-Nordtvedt et al., 2008). Knowledge transfer is defined by Kumar and Ganesh (2009) as activities of exchanging explicit or tacit knowledge between two agents, during which one agent receive and apply the knowledge provided by the other agent. The agents could be an individual, team/department or an organization (Joshi et al., 2007). In the literature, knowledge transfer has been given various but related labels such as ‗knowledge sharing, ‗knowledge flows', ‗knowledge acquisition' and ‗knowledge mobilization ' (Carmel and Nicholson, 2005;Gosain, 2007;Renzl, 2008;van Wijk et al, 2008).Information systems (IS) outsourcing, where a client organization contract out some or all of its IS functions to one or more external vendors (Lee, 2001), has been regards as an important business strategy for client organizations to transfer new technical and business knowledge from the vendors (e.g. Ko et al., 2005;Tafti, 2007, Blumenberg et al., 2009). Wang et al. (2008 argued that outsourcing IS to high quality vendors has the potential to transfer knowledge that are costly or hard to develop in-house. Furthermore, IS outsourcing allows the client organizations to renew its technical and business knowledge base in order to achieve congruence with changing business environment (Bandyopadhyay and Pathak, 2007). In their (2005) reported that client organizations acquire new implementation, operational and maintenance knowledge from their consultants, so they can learn and later maintain the system independent of the consultant's team. Edguer and Pervan (2004) found that many firms are increasingly looking at IS outsourcing as a means of transferring and leveraging the vendors' superior technical and business knowledge and benefiting complementary skills and specialist expertise that are not available within the organization's boundaries. Knowledge transfer from vendors to clients in IS outsourcing projects occur through a variety of mechanisms. These mechanisms include manuals, personal movement, training, observation, presentations and close interaction with vendors' IS staff (Nicholson and Sahay, 2004;Xu et al., 2006;Chua and Pan, 2008) Knowle...

The main aim of this paper is to develop an amalgamated conceptual model of technology acceptance that explains how individual, social, cultural and organizational factors affect the students' acceptance and usage behaviour of the Web-based learning systems. More specifically, the proposed model extends the Technology Acceptance Model (TAM) to include four constructs namely, Quality of work life; Social Norm; Facilitating Conditions and self-efficacy. In addition, Individual differences-namely, age, gender, and experience-are hypothesized to moderate the effects of these constructs on behavioral intention and technology use. A comprehensive understanding of this model will provide valuable insights into the factors that influence the acceptance or resistance of web-based learning system by intended users and offers opportunities for future research in understanding the acceptance of technology. Further, understanding these variables is helpful for instructors to design meaningful educational activities to promote student knowledge construction and make learning more effective and appealing.