How can action research be made more rigorous? We discuss in this paper action research, positivism and some major criticisms of action research by positivists. We then examine issues relating the conduct of IS research in organisations through multiple iterations in the action research cycle proposed by Susman and Evered. We argue that the progress through iterations allows the researcher to gradually broaden the research scope and in consequence add generality to the research findings. A brief illustrative case is provided with a study on groupware introduction in a large civil engineering company. In the light of this illustrative case we contend that effective application of the iterative approach to action research has the potential to bring research rigour up closer to standards acceptable by positivists and yet preserve the elements that characterise action research as such.

If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/ authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this paper is to explore the impact of national culture on the structured knowledge transfer from a US-based (onshore) technical support center to an offshore support center in China.Design/methodology/approach -The research was conducted as an interpretive case study. Three techniques (i.e. document review, participant observation, and semi-structured interviews) were employed for data collection in the field.Findings -The findings identify that knowledge tacitness, knowledge gaps, cultural and communication difficulties and weak relationships were the critical barriers to successful knowledge transfer in a cross-cultural knowledge transfer context. It was found that, when a provider and a recipient are located in different individualism/collectivism, power distance, and uncertainty avoidance cultural dimensions, there will be a reduced likelihood of successful knowledge transfer in a structured knowledge transfer process. However, peer-to-peer help, close relationships and proactive learning may assist in decreasing the knowledge transfer difficulties.Research limitations/implications -The research was limited to one organization in one industry (the IT support industry) and in one country (China). There could be both industry-specific issues and national cultural issues that may affect the findings and conclusions. However, the paper has important practical implications for organizations that are trying to carry out transfer of organizational knowledge or to acquire organizational knowledge in a cross-cultural business context.Originality/value -The findings provide insight into the cultural issues implicated in the structured knowledge transfer process, when a knowledge provider and a recipient are from different cultural dimensions, as well as offering more general insight into the mechanism of knowledge transfer in the cross-cultural business context.

Many techniques have been developed for learning rules and relationships automatically from diverse data sets, to simplify the often tedious and error-prone process of acquiring knowledge from empirical data. While these techniques are plausible, theoretically wellfounded, and perform well on more or less artificial test data sets, they depend on their ability to make sense of real-world data. This paper describes a project that is applying a range of machine learning strategies to problems in agriculture and horticulture. We briefly survey some of the techniques emerging from machine learning research, describe a software workbench for experimenting with a variety of techniques on real-world data sets, and describe a case study of dairy herd management in which culling rules were inferred from a medium-sized database of herd information.

Purpose This paper aims to explore the relationship between face-to-face social networks and knowledge sharing. Design/methodology/approach Qualitative data gathered through 25 semi-structured interviews in five manufacturing firms were collected and analysed. A grounded theory approach was used to analyse the data, which was supported through NVivo qualitative data analysis software. Findings The results reveal that face-to-face social networks facilitate knowledge sharing in diverse ways. These include the use of multiple communication styles, brainstorming and problem-solving, learning and teaching, training, consultations and employee rotation. Practical implications The findings of this research are expected to help practitioners to comprehend the big picture and scope of the steps they take to facilitate knowledge sharing in organisations. Viewing knowledge sharing from a holistic perspective can help practitioners comprehend how face-to-face knowledge sharing fits with and complements other knowledge-sharing channels, such as electronic social media and document repositories. In addition, through face-to-face social networks, practitioners can leverage work groups to increase knowledge sharing, meaning that potential cost savings and improved work practices can be achieved. Originality/value For researchers, three new models are developed which provide new insights into the nature of the relationship between face-to-face social networks and knowledge sharing. The first model relates to brainstorming and problem-solving, the second to knowledge levels and the direction of learning and teaching and the third to factors influencing social networks and knowledge sharing.

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