We present a review and analysis of the rich body of research on the adoption and diffusion of IT-based innovations by individuals and organizations. Our review analyzes 48 empirical studies on individual and 51 studies on organizational IT adoption published between 1992 and 2003. In total, the sample contains 135 independent variables, eight dependent variables, and 505 relationships between independent and dependent variables. Furthermore, our sample includes both quantitative and qualitative studies. We were able to include qualitative studies because of a unique coding scheme, which can easily be replicated in other reviews. We use this sample to assess predictors, linkages, and biases in individual and organizational IT adoption research. The best predictors of individual IT adoption include Perceived Usefulness, Top Management Support, Computer Experience, Behavioral Intention, and User Support. The best predictors of IT adoption by organizations were Top Management Support, External Pressure, Professionalism of the IS Unit, and External Information Sources. At the level of independent variables, Top Management Support stands as the main linkage between individual and organizational IT adoption. But at an aggregate level, two collections of independent variables were good predictors of both individual and organizational IT adoption. These were innovation characteristics and organizational characteristics. Thus, we can consistently say that generic characteristics of the innovation and characteristics of the organization are strong predictors of IT adoption by both individuals and organizations. Based on an assessment of the predictors, linkages, and known biases, we prescribe 10 areas for further exploration.

No abstract

An enormous amount of information has been produced about the IT outsourcing phenomenon over the last 20 years, but one has to look to the academic literature for consistent, objective, and reliable research approaches and analyses. Our review finds that, In practice, the academic literature on IT outsourcing has very much honored both rigor and relevance In the ways In which research has been conducted. Our central purpose In the review was to answer two research questions: What has the empirical academic literature found about information technology outsourcing (ITO) decisions and outcomes? What are the gaps In knowledge to consider In future ITO research? To answer these questions, we examined 164 empirical ITO articles published between 1992 and 2010 In 50 journals. Adapting a method used by Jeyaraj et al. (2006), we encapsulated this vast empirical literature on ITO In a way that was concise, meaningful, and helpful to researchers. We coded 36 dependent variables, 138 independent variables, and 741 relationships between Independent and dependent variables. By extracting the best evidence, we developed two models of outsourcing: one model addressed ITO decisions and one model addressed ITO outcomes. The model of ITO decisions includes Independent variables associated with motives to outsource, transaction attributes, client firm characteristics, and influence sources. The model of ITO outcomes includes Independent variables associated with client and supplier capabilities, relationship characteristics, contractual governance, decision characteristics, and transaction attributes. We also examined the interactions among broad categories of variables and the learning curve effects resulting from feedback loops. Overall, ITO researchers have a broad and deep understanding of ITO. However, the field continues to evolve as clients and suppliers on every inhabited continent participate actively in the global sourcing community. There is still much research yet to be done. We reviewed recent studies that have identified gaps in current knowledge and proposed future paths of research pertaining to strategic motivations, environmental influences, dynamic interactions, configurational and portfolio approaches, global destinations, emerging models, reference theory extension, and grounded theory development.

We now differentiate between the requirements for new and revised submissions. You may choose to submit your manuscript as a single Word or PDF file to be used in the refereeing process. Only when your paper is at the revision stage, will you be requested to put your paper in to a 'correct format' for acceptance and provide the items required for the publication of your article. To find out more, please visit the Preparation section below. Article structure Subdivision-unnumbered sections Divide your article into clearly defined sections. Each subsection is given a brief heading. Each heading should appear on its own separate line. Subsections should be used as much as possible when crossreferencing text: refer to the subsection by heading as opposed to simply 'the text'. Highlights Highlights are mandatory for this journal as they help increase the discoverability of your article via search engines. They consist of a short collection of bullet points that capture the novel results of your research as well as new methods that were used during the study (if any). Please have a look at the examples here: example Highlights. Highlights should be submitted in a separate editable file in the online submission system. Please use 'Highlights' in the file name and include 3 to 5 bullet points (maximum 85 characters, including spaces, per bullet point).

No abstract