Communities of implementation

The concept of communities of practice (CoPs) has rapidly gained ground in fields such as knowledge management and organisational learning since it was first identified by Lave and Wenger (1991) and Brown and Duguid (1991). In this article, we consider a related concept that we have entitled “communities of implementation.” Communities of implementation (CoIs) are similar to communities of practice in that they offer an opportunity for a collection of individuals to support each other and share knowledge in a dynamic environment and on a topic in which they share interest. In addition, and to differentiate them from CoPs, a community of implementation extends the responsibilities of a CoP by having as its focus the implementation of a programme of change. This may well extend to designing the change programme. Thus, whereas a main purpose of a CoP is to satisfy “a real need to know what each other knows” (Skyrme, 1999) in an informal way, we argue that a main purpose of a community of implementation is to “pool individual knowledge (including contacts and ways of getting things done) to stimulate collective enthusiasm in order to take more informed purposeful action for which the members are responsible.” Individual and collective responsibility and accountability for successfully implementing the actions/change programme is a key feature of a community of implementation. Without these pressures the members might lower the priority of implementation, allowing competing priorities to dominate their attention and resources. Without responsibility and accountability, the result is likely to be (at best) an organisation which has not begun a change programme, or (at worst) an organisation which is stuck halfway through another failing initiative. To achieve these additional objectives beyond those of a CoP, the CoI needs to provide heightened support to its members. In fact often the members will collectively strategise the development and implementation of the change programme they are leading in the organisation. Other concepts similar to CoPs have appeared in the literature, for example “communities of knowing” (Boland & Tenkasi, 1995), but none have a specific focus on implementation. Perhaps the closest example of a CoI, as suggested by our definition, is reported by Karsten, Lyytinen, Hurskainen, and Koskelainen (2001) who describe a CoP in a paper machinery manufacturer which seems to have the necessary focus on implementation. The theoretical aspects of this article will explore the relationship between CoPs and CoIs, and the needs for different arrangements for a CoI. The practical aspect of this article will consist of a report on a case study of a CoI that was successful in its implementation of a programme of change that aimed to improve its organisation’s knowledge management activities. Over two years the CoI implemented a suite of complementary actions across the organisation. These actions transformed the organisation and moved it towards achieving its ‘core values’ and overall objectives. The article will explore: the activities that formed and gelled the community, the role of the community in the implementation of actions, and experiences from key members of this community on its success and potential improvements.

Learning sentiment classification model from labeled features

We propose a novel framework where an initial classifier is learned by incorporating prior information extracted from an existing sentiment lexicon. Preferences on expectations of sentiment labels of those lexicon words are expressed using generalized expectation criteria. Documents classified with high confidence are then used as pseudo-labeled examples for automatical domain-specific feature acquisition. The word-class distributions of such self-learned features are estimated from the pseudo-labeled examples and are used to train another classifier by constraining the model's predictions on unlabeled instances. Experiments on both the movie review data and the multi-domain sentiment dataset show that our approach attains comparable or better performance than exiting weakly-supervised sentiment classification methods despite using no labeled documents.

A novel framework of training hidden Markov support vector machines from lightly-annotated data

Natural language understanding (NLU) aims to map sentences to their semantic mean representations. Statistical approaches to NLU normally require fully-annotated training data where each sentence is paired with its word-level semantic annotations. In this paper, we propose a novel learning framework which trains the Hidden Markov Support Vector Machines (HM-SVMs) without the use of expensive fully-annotated data. In particular, our learning approach takes as input a training set of sentences labeled with abstract semantic annotations encoding underlying embedded structural relations and automatically induces derivation rules that map sentences to their semantic meaning representations. The proposed approach has been tested on the DARPA Communicator Data and achieved 93.18% in F-measure, which outperforms the previously proposed approaches of training the hidden vector state model or conditional random fields from unaligned data, with a relative error reduction rate of 43.3% and 10.6% being achieved.

Joint sentiment/topic model for sentiment analysis

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework based on Latent Dirichlet Allocation (LDA), called joint sentiment/topic model (JST), which detects sentiment and topic simultaneously from text. Unlike other machine learning approaches to sentiment classification which often require labeled corpora for classifier training, the proposed JST model is fully unsupervised. The model has been evaluated on the movie review dataset to classify the review sentiment polarity and minimum prior information have also been explored to further improve the sentiment classification accuracy. Preliminary experiments have shown promising results achieved by JST.

Originator or propagator? Incorporating social role theory into topic models for twitter content analysis

A large number of studies have been devoted to modeling the contents and interactions between users on Twitter. In this paper, we propose a method inspired from Social Role Theory (SRT), which assumes that a user behaves differently in different roles in the generation process of Twitter content. We consider the two most distinctive social roles on Twitter: originator and propagator, who respectively posts original messages and retweets or forwards the messages from others. In addition, we also consider role-specific social interactions, especially implicit interactions between users who share some common interests. All the above elements are integrated into a novel regularized topic model. We evaluate the proposed method on real Twitter data. The results show that our method is more effective than the existing ones which do not distinguish social roles. Copyright 2013 ACM.

Knowledge management in a New Zealand tree farming company:Ambiguity and resistance to the "technology solution"

Purpose - Managers at the company attempt to implement a knowledge management information system in an attempt to avoid loss of expertise while improving control and efficiency. The paper seeks to explore the implications of the technological solution to employees within the company. Design/methodology/approach - The paper reports qualitative research conducted in a single organization. Evidence is presented in the form of interview extracts. Findings - The case section of the paper presents the accounts of organizational participants. The accounts reveal the workers' reactions to the technology-based system and something of their strategies of resistance to the system. These accounts also provide glimpses of the identity construction engaged in by these knowledge workers. The setting for the research is in a knowledge-intensive primary industry. Research was conducted through observation and interviews. Research limitations/implications - The issues identified are explored in a single case-study setting. Future research could look at the relevance of the findings to other settings. Practical implications - The case evidence presented indicates some of the complexity of implementation of information systems in organizations. This could certainly be seen as more evidence of the uncertainty associated with organizational change and of the need for managers not to expect an easy adoption of intrusive IT solutions. Originality/value - This paper adds empirical insight to a largely conceptual literature. © Emerald Group Publishing Limited.

eHealth as a challenge to `expert' power: a focus group study of internet use for health information and management

Objective To investigate current use of the internet and eHealth amongst adults. Design Focus groups were conducted to explore participants' attitudes to and reasons for health internet use. Main outcome measures The focus group data were analysed and interpreted using thematic analysis. Results Three superordinate themes exploring eHealth behaviours were identified: decline in expert authority, pervasiveness of health information on the internet and empowerment. Results showed participants enjoyed the immediate benefits of eHealth information and felt empowered by increased knowledge, but they would be reluctant to lose face-to-face consultations with their GP. Conclusions Our findings illustrate changes in patient identity and a decline in expert authority with ramifications for the practitioner–patient relationship and subsequent implications for health management more generally.

Knowledge management and risk management

This article takes the perspective that risk knowledge and the activities related to RM practice can benefit from the implementation of KM processes and systems, to produce a better enterprise wide implementation of risk management. Both in the information systems discipline and elsewhere, there has been a trend towards greater integration and consolidation in the management of organizations. Some examples of this are: Enterprise Resource Planning (Stevens, 2003), Enterprise Architecture (Zachmann, 1996) and Enterprise Content Management (Smith & McKeen, 2003). Similarly, risk management is evolving into Enterprise Risk Management. KM’s importance in breaking down silos within an organization can help it to do so.

Knowledge management and enterprise risk management implementation in financial services

This paper starts from the viewpoint that enterprise risk management is a specific application of knowledge in order to control deviations from strategic objectives, shareholders’ values and stakeholders’ relationships. This study is looking for insights into how the application of knowledge management processes can improve the implementation of enterprise risk management. This article presents the preliminary results of a survey on this topic carried out in the financial services sector, extending a previous pilot study that was in retail banking only. Five hypotheses about the relationship of knowledge management variables to the perceived value of ERM implementation were considered. The survey results show that the two people-related variables, perceived quality of communication among groups and perceived quality of knowledge sharing were positively associated with the perceived value of ERM implementation. However, the results did not support a positive association for the three variables more related to technology, namely network capacity for connecting people (which was marginally significant), risk management information system functionality and perceived integration of the information systems. Perceived quality of communication among groups appeared to be clearly the most significant of these five factors in affecting the perceived value of ERM implementation.

Dissidents with an innovation cause? Non-institutionalized actors’ online social knowledge sharing, solution-finding tensions and technology management innovation

Purpose – Traditionally, most studies focus on institutionalized management-driven actors to understand technology management innovation. The purpose of this paper is to argue that there is a need for research to study the nature and role of dissident non-institutionalized actors’ (i.e. outsourced web designers and rapid application software developers). The authors propose that through online social knowledge sharing, non-institutionalized actors’ solution-finding tensions enable technology management innovation. Design/methodology/approach – A synthesis of the literature and an analysis of the data (21 interviews) provided insights in three areas of solution-finding tensions enabling management innovation. The authors frame the analysis on the peripherally deviant work and the nature of the ways that dissident non-institutionalized actors deviate from their clients (understood as the firm) original contracted objectives. Findings – The findings provide insights into the productive role of solution-finding tensions in enabling opportunities for management service innovation. Furthermore, deviant practices that leverage non-institutionalized actors’ online social knowledge to fulfill customers’ requirements are not interpreted negatively, but as a positive willingness to proactively explore alternative paths. Research limitations/implications – The findings demonstrate the importance of dissident non-institutionalized actors in technology management innovation. However, this work is based on a single country (USA) and additional research is needed to validate and generalize the findings in other cultural and institutional settings. Originality/value – This paper provides new insights into the perceptions of dissident non-institutionalized actors in the practice of IT managerial decision making. The work departs from, but also extends, the previous literature, demonstrating that peripherally deviant work in solution-finding practice creates tensions, enabling management innovation between IT providers and users.

User-centred ontology learning for knowledge management

Automatic ontology building is a vital issue in many fields where they are currently built manually. This paper presents a user-centred methodology for ontology construction based on the use of Machine Learning and Natural Language Processing. In our approach, the user selects a corpus of texts and sketches a preliminary ontology (or selects an existing one) for a domain with a preliminary vocabulary associated to the elements in the ontology (lexicalisations). Examples of sentences involving such lexicalisation (e.g. ISA relation) in the corpus are automatically retrieved by the system. Retrieved examples are validated by the user and used by an adaptive Information Extraction system to generate patterns that discover other lexicalisations of the same objects in the ontology, possibly identifying new concepts or relations. New instances are added to the existing ontology or used to tune it. This process is repeated until a satisfactory ontology is obtained. The methodology largely automates the ontology construction process and the output is an ontology with an associated trained leaner to be used for further ontology modifications.

Knowledge acquisition for knowledge management: position paper

With this paper, we propose a set of techniques to largely automate the process of KA, by using technologies based on Information Extraction (IE) , Information Retrieval and Natural Language Processing. We aim to reduce all the impeding factors mention above and thereby contribute to the wider utility of the knowledge management tools. In particular we intend to reduce the introspection of knowledge engineers or the extended elicitations of knowledge from experts by extensive textual analysis using a variety of methods and tools, as texts are largely available and in them - we believe - lies most of an organization's memory.

Knowledge management applied to enterprise risk management

Risk and knowledge are two concepts and components of business management which have so far been studied almost independently. This is especially true where risk management (RM) is conceived mainly in financial terms, as for example, in the financial institutions sector. Financial institutions are affected by internal and external changes with the consequent accommodation to new business models, new regulations and new global competition that includes new big players. These changes induce financial institutions to develop different methodologies for managing risk, such as the enterprise risk management (ERM) approach, in order to adopt a holistic view of risk management and, consequently, to deal with different types of risk, levels of risk appetite, and policies in risk management. However, the methodologies for analysing risk do not explicitly include knowledge management (KM). This research examines the potential relationships between KM and two RM concepts: perceived quality of risk control and perceived value of ERM. To fulfill the objective of identifying how KM concepts can have a positive influence on some RM concepts, a literature review of KM and its processes and RM and its processes was performed. From this literature review eight hypotheses were analysed using a classification into people, process and technology variables. The data for this research was gathered from a survey applied to risk management employees in financial institutions and 121 answers were analysed. The analysis of the data was based on multivariate techniques, more specifically stepwise regression analysis. The results showed that the perceived quality of risk control is significantly associated with the variables: perceived quality of risk knowledge sharing, perceived quality of communication among people, web channel functionality, and risk management information system functionality. However, the relationships of the KM variables to the perceived value of ERM are not identified because of the low performance of the models describing these relationships. The analysis reveals important insights into the potential KM support to RM such as: the better adoption of KM people and technology actions, the better the perceived quality of risk control. Equally, the results suggest that the quality of risk control and the benefits of ERM follow different patterns given that there is no correlation between both concepts and the distinct influence of the KM variables in each concept. The ERM scenario is different from that of risk control because ERM, as an answer to RM failures and adaptation to new regulation in financial institutions, has led organizations to adopt new processes, technologies, and governance models. Thus, the search for factors influencing the perceived value of ERM implementation needs additional analysis because what is improved in RM processes individually is not having the same effect on the perceived value of ERM. Based on these model results and the literature review the basis of the ERKMAS (Enterprise Risk Knowledge Management System) is presented.