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Journal Cover Journal of Knowledge Management
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   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1367-3270
   Published by Emerald Homepage  [335 journals]
  • The concepts of big data applied in personal knowledge management
    • Abstract: Journal of Knowledge Management, Volume 21, Issue 1, February 2017.
      Purpose The purpose of this paper is to demonstrate the applications of big data in personal knowledge management. Design/methodology/approach Five conventional knowledge management dimensions, including the value of data, data collection, data storage, data application, and data presentation, were applied to integrating big data in the context of personal knowledge management. Findings This study concludes that time management, computer usage efficiency management, mobile device usage behavior management, health management and browser surfing management are areas where big data can be applied to personal knowledge management. Originality/value While the literature discusses personal knowledge management without considering the impact of big data, this article aims to extend existing knowledge by demonstrating the application of big data in personal knowledge management.
      Citation: Journal of Knowledge Management
      PubDate: 2017-01-18T12:41:14Z
      DOI: 10.1108/JKM-07-2015-0298
  • GUEST EDITORIAL Does big data mean big knowledge? Knowledge management
           perspectives on big data and analytics
    • Abstract: Journal of Knowledge Management, Volume 21, Issue 1, February 2017.
      Purpose This viewpoint article makes the case that the field of Knowledge Management (KM) must respond to the significant changes that big data/analytics is bringing to operationalizing the production of organizational data and information. Design/methodology/approach This article expresses the opinions of the co-editors of the special Journal of Knowledge Management issue, Does Big Data mean Big Knowledge?: KM Perspectives on Big Data and Analytics. Findings A Big Data/Analytics-Knowledge Management (BDA-KM) model is proposed that illustrates the centrality of knowledge as the guiding principle in the use of big data/analytics in organizations. Research limitations/implications This is an opinion piece and the proposed model still needs to be empirically verified. Practical implications It is suggested that academic and practitioners in KM must be capable of controlling the application of big data/analytics and calls for further research investigating how KM can conceptually and operationally use and integrate big data/analytics to foster organizational knowledge for better decision-making and organizational value creation. Originality/value The Big Data/Analytics-Knowledge Management (BDA-KM) model is one of the early models placing knowledge as the primary consideration in the successful organizational use of Big Data/analytics.
      Citation: Journal of Knowledge Management
      PubDate: 2017-01-18T12:41:13Z
      DOI: 10.1108/JKM-08-2016-0339
  • Information and reformation in KM systems: big data and strategic
    • Abstract: Journal of Knowledge Management, Volume 21, Issue 1, February 2017.
      Purpose Advanced analytics are becoming increasingly critical in making strategic decisions in any organization from the private to public sectors, and from for-profit companies to not-for-profit organizations. Despite the growing importance of capturing, sharing and implementing people’s knowledge in organizations, it is still unclear how big data and the need for advanced analytics can inform and, if necessary, reform the design and implementation of Knowledge Management systems. Design/methodology/approach To address this gap a combined approach has been applied. The KM and data analysis systems implemented by companies were analysed, and the analysis was complemented by a review of the extant literature. Findings Four types of data-based decisions as well as a set of ground rules are identified towards enabling KM systems to handle big data and advanced analytics. Practical implications The paper proposes a practical framework that takes into account the diverse combinations of data-based decisions. Suggestions are provided about how KM systems can be reformed in order to facilitate the incorporation of big data and advanced analytics into organizations’ strategic decision-making. Originality/value The first typology of data-based decision-making considering advanced analytics.
      Citation: Journal of Knowledge Management
      PubDate: 2017-01-18T12:41:05Z
      DOI: 10.1108/JKM-07-2015-0293
  • Facilitating knowledge management through filtered big data: SME
           competitiveness in an agri-food sector
    • Abstract: Journal of Knowledge Management, Volume 21, Issue 1, February 2017.
      Purpose To critique a facilitated knowledge management process that utilises filtered big data, and specifically, the processes effectiveness in overcoming barriers to SMEs’ use of big data, the processes enablement of SME engagement with and use of big data, and the processes effect on SME competitiveness, within an agri-food sector. Design/methodology/approach From 300 participant firms, SME owner-managers representing seven longitudinal case studies were contacted by the facilitator at least once-monthly over six months. Findings Results indicate that explicit and tacit knowledge can be enhanced when SMEs have access to a facilitated programme that analyses, packages, and explains big data consumer analytics captured by a large pillar firm in a food network. Additionally, big data and knowledge are mutually exclusive unless effective knowledge management processes are implemented. Several barriers to knowledge acquisition and application stem from SME resource limitations, strategic orientation, and asymmetrical power relationships within-network. Research limitations/implications By using Dunnhumby data, this study captured the impact of only one form of big data, consumer analytics. However, this is a significant dataset for SME agri-food businesses. Additionally, although the SMEs were based in only one UK region, Northern Ireland, there is wide scope for future research across multiple UK regions with the same Dunnhumby dataset. Practical implications Originality/value The study demonstrates the potential relevance of big data to SMEs’ activities and developments, explicitly identifying that realising this potential requires the data to be filtered and presented as market relevant information that engages SMEs, recognises relationship dynamics, and supports learning through feedback and two-way dialogue. This is the first study that empirically analyses filtered big data and SME competitiveness. The examination of relationship dynamics also overcomes existing literature limitations where SMEs’ constraints are seen as the prime factor restricting knowledge transfer.
      Citation: Journal of Knowledge Management
      PubDate: 2017-01-18T12:40:55Z
      DOI: 10.1108/JKM-08-2016-0357
  • How the Internet of Things can help knowledge management: a case study
           from the automotive domain
    • Abstract: Journal of Knowledge Management, Volume 21, Issue 1, February 2017.
      Purpose Current knowledge management systems cannot be used effectively for decision making because of the lack of real time data. This paper discusses how knowledge management (KM) can benefit by embedding Internet of Things (IoT). Design/methodology/approach The paper discusses how IoT can help KM to capture data and convert data into knowledge to improve the parking service in transportation using a case study. Findings Our case study related to intelligent parking service supported by IoT devices of vehicles shows that KM can play a role in turning the incoming big data collected from IoT devices into useful knowledge more quickly and effectively. Originality/value Our literature review shows that there are few papers discussing how knowledge management can benefit by embedding IoT and processing incoming big data collected from IoT devices. The case study we developed provides evidence to explain how IoT can help KM to capture big data and convert big data into knowledge to improve the parking service in transportation.
      Citation: Journal of Knowledge Management
      PubDate: 2017-01-18T12:40:52Z
      DOI: 10.1108/JKM-07-2015-0291
  • Cognitive big data: survey and review on big data research and its
           implications. What is really ‘new’ in big data?
    • Abstract: Journal of Knowledge Management, Volume 21, Issue 1, February 2017.
      Purpose What is really ‘new’ in big data? Big data seems to be a hyped-up concept that has emerged during recent years. But it requires thorough discussion beyond the common 4V (velocity, volume, veracity and variety) approach. Design/methodology/approach We established an expert think tank to discuss the notion of big data, identify new characteristics, and re-think what really is new in the idea of big data, by analysing over 60 literature resources. We identified typical baseline scenarios (traffic, business processes, retail, health, and social media) as a starting point, from which we explored the notion of big data from different perspectives. Findings We concluded that the idea of Big Data is simply not new, and recognised the need to re-think a new approach towards Big Data. We also introduced a 5-Trait Framework for ‘Cognitive Big Data’, socio-technical system, data space, data richness, knowledge management/decision making, and visualization / sensory presentation. Research limitations/implications Our viewpoint is centred on cognitive processes as KM process. Practical implications Data needs to be made available in an understandable form, for the right application context, and in the right portion size that it can be turned into knowledge and eventually wisdom. We need to know about data that can be ignored, data that we are not aware of (dark data), and data that can be fully utilised for analysis (light data). In the foreground is human and machine understandability.’ – In form of Cognitive big data. Originality/value Introduction of cognitive big data as concept and framework.
      Citation: Journal of Knowledge Management
      PubDate: 2017-01-18T12:40:52Z
      DOI: 10.1108/JKM-07-2016-0307
  • Big data systems: knowledge transfer or intelligence insights?
    • Abstract: Journal of Knowledge Management, Volume 21, Issue 1, February 2017.
      Purpose This paper brings together existing theory from knowledge management, competitive intelligence, and big data analytics in order to develop a more comprehensive view of the full range of intangible assets (data, information, knowledge, intelligence). By doing so, the interactions of the intangibles are better understood and recommendations can be made for the appropriate structure of big data systems in different circumstances. Metrics are also applied in order to illustrate how we can identify and understand what these different circumstances might look like. Design/methodology/approach Chiefly conceptual, combining theory from multiple disciplines enhanced with practical applications. Illustrative data drawn from other empirical work is applied to illustrate some concepts. Findings Theory suggests that knowledge management theory is particularly useful in guiding big data system installations that focus primarily on the transfer of data/information. For big data systems focused on analytical insights, competitive intelligence theory might be the better match as the system structures are actually quite similar. Practical implications Though the guidelines are general, practitioners should be able to evaluate their own situations and perhaps make better decisions about the direction of their big data systems. One can make the case that all the disciplines have something to add to improving how intangibles are deployed and applied and that improving coordination between knowledge management and analytics/intelligence functions will help all intangibles systems to work more effectively. Originality/value To our knowledge, very few scholars work in this area, at the intersection of multiple types of intangible assets. The metrics are unique, especially in their scale and attachment to theory, allowing insights that provide more clarity to scholars and practical direction to industry.
      Citation: Journal of Knowledge Management
      PubDate: 2017-01-18T12:40:44Z
      DOI: 10.1108/JKM-07-2015-0300
  • Big data text analytics: an enabler of knowledge management
    • Abstract: Journal of Knowledge Management, Volume 21, Issue 1, February 2017.
      Purpose The purpose of this paper is to examine the role of big data text analytics as an enabler of knowledge management. The paper argues big data text analytics represents an important means to visualise and analyse data, especially unstructured data, which has the potential to improve knowledge management within organizations. Design/methodology/approach The study uses text analytics to review 196 articles published in two of the leading knowledge management journals - the Journal of Knowledge Management and the Journal of Knowledge Management Research & Practice in 2013 and 2014. The text analytics approach is used to process, extract and analyse the 196 papers to identify trends in terms of keywords, topics and keyword/topic clusters to show the utility of big data text analytics. Findings The findings show how big data text analytics can be a key enabler role in knowledge management. Drawing on the 196 articles analysed, the paper shows the power of big data-oriented text analytics tools in supporting knowledge management through the visualization of data. In this way we highlight the nature and quality of the knowledge generated through this method for efficient knowledge management in developing a competitive advantage. Research limitations/implications The research has important implications concerning the role of big data text analytics in knowledge management, and specifically the nature and quality of knowledge produced using text analytics. We use text analytics to exemplify the value of big data in the context of knowledge management, and highlight how future studies could develop and extend these findings in different contexts. Practical implications Results contribute to understanding the role of big data text analytics as means to enhance the effectiveness of knowledge management. The paper provides important insights that can be applied to different business functions, from supply chain management to marketing management, to support knowledge management through the use of big data text analytics. Originality/value The study demonstrates the practical application of the big data tools for data visualisation and with it improving knowledge management
      Citation: Journal of Knowledge Management
      PubDate: 2017-01-18T12:40:33Z
      DOI: 10.1108/JKM-06-2015-0238
  • Davenport and Prusak on KM and big data/analytics: interview with David J.
    • Abstract: Journal of Knowledge Management, Volume 21, Issue 1, February 2017.
      Purpose Larry Prusak and Tom Davenport have long been leading voices in the Knowledge Management field. This interview explores their views on the relationship between KM and big data/analytics. Design/methodology/approach An interview was conducted by email with Larry Prusak and Tom Davenport in 2015 and updated in 2016 Findings Prusak and Davenport hold differing views on the role of KM today. They also see the relationship between KM and big data/analytics somewhat differently. Research limitations/implications Davenport, in particular, has much to say on how big data/analytics can be best utilized by business as well as its potential risks. Originality/value It is important to understand how two of the most serious KM thinkers since the early years of KM understand the relationship between big data/analytics, KM and organizations. Their views can help shape thinking in these fields.
      Citation: Journal of Knowledge Management
      PubDate: 2017-01-18T12:40:30Z
      DOI: 10.1108/JKM-08-2016-0329
  • Creation of knowledge-added concept maps: time augmention via pairwise
           temporal analysis
    • Abstract: Journal of Knowledge Management, Volume 21, Issue 1, February 2017.
      Purpose Although acknowledged as a principal dimension in the context of text mining, time has yet to be formally incorporated into the process of visually representing the relationships between keywords in a knowledge domain. This paper aims to develop and validate the feasibility of adding temporal knowledge to a concept map via pair-wise temporal analysis (PTA). Design/methodology/approach The paper presents a temporal trend detection algorithm - vector space model (VSM) - designed to utilize objective quantitative pair-wise temporal operators to automatically detect co-occurring hot concepts. This pair-wise temporal analysis approach is demonstrated and validated without loss of generality for a spectrum of information technologies. Findings The rigours validation study shows that the resulting temporal assessments are highly correlated with subjective assessments of experts (n=136), indicating substantial reliability-of-agreement measures and average predictive validity above 85%. Practical implications Using massive amounts of textual documents available on the web to generate a concept map first and then add temporal knowledge, the contribution of this work is emphasized and magnified against the current growing attention to big data analytics. Originality/value This paper proposes a novel knowledge discovery method to improve text-based concept maps via detection and representation of temporal relationships in concept map (i.e., semantic graph). The originality and value of the proposed method is highlighted in comparison to other knowledge discovery methods.
      Citation: Journal of Knowledge Management
      PubDate: 2017-01-18T12:40:30Z
      DOI: 10.1108/JKM-07-2016-0279
  • Interrelationship between big data and knowledge management: an
           exploratory study in the oil and gas sector
    • Abstract: Journal of Knowledge Management, Volume 21, Issue 1, February 2017.
      Purpose The purpose of this article is to explore the relationship between big data and knowledge management (KM). Design/methodology/approach The study adopts a qualitative research methodology and a case study approach was followed by conducting 9 semi structured interviews with open ended and probing questions. Findings Useful predictive knowledge can be generated through big data to help companies improve their knowledge management capability and make effective decisions. Moreover, combination of tacit knowledge of relevant staff with explicit knowledge obtained from big data improvises the decision making ability. Research limitations/implications The focus of the study was on oil and gas sector and thus the research results may lack generalisability. Originality/value This paper fulfills an identified need of exploring the relationship between big data and knowledge management which hasn't been discussed much in the literature.
      Citation: Journal of Knowledge Management
      PubDate: 2017-01-18T12:40:25Z
      DOI: 10.1108/JKM-07-2016-0262
  • An exploration of contemporary organizational artifacts and routines in a
           sustainable excellence context
    • Abstract: Journal of Knowledge Management, Volume 21, Issue 1, February 2017.
      Purpose Organizations and their members operate in increasingly complex, dynamic and even disruptive environments, with risk and uncertainty being major challenges. To that effect data, information, knowledge, and respective competences are increasingly instrumental in enabling and sustaining organizational intelligence that translates into resilience in the shorter and sustainable excellence in the longer term. Therefore, the aim of this paper is to explore the role of the artifacts and routines in a sustainable organizational excellence context Design/methodology/approach An extensive literature review was used to develop the context of the paper, focusing on big data and organizational intelligence for enterprise excellence and resilience. In addition, a thematic literature review method was used to study the role and impacts of routines and artifacts in organizational change, policies, structure, and performance. Findings Although many traditional management practices retain their validity, knowledge management must give a clearer view of the existing connection between firm-level competitive advantage in open economies flows and difficult-to-use knowledge assets. The proposed framework studies knowledge exploration and knowledge exploitation as organizational phenomena opposed and mutually incompatible. Originality/value The paper presents a first attempt to study the linkages of organizational routines and artifacts as a cycle wherein knowledge acquisition and learning competencies form and enhance a firm’s organizational intelligence, leading to robust competitiveness and sustainable entrepreneurship.
      Citation: Journal of Knowledge Management
      PubDate: 2017-01-18T12:40:24Z
      DOI: 10.1108/JKM-10-2015-0366
  • Dave Snowden on KM and big data/analytics: interview with David J. Pauleen
    • Abstract: Journal of Knowledge Management, Volume 21, Issue 1, February 2017.
      Purpose Dave Snowden has been an important voice in knowledge management over the years. As the founder and chief scientific officer of Cognitive Edge, a company focused on the development of the theory and practice of social complexity, he offers informative views on the relationship between big data/analytics and KM. Design/methodology/approach A face to face interview was held with Dave Snowden in May 2015 in Auckland, New Zealand. Findings According to Snowden, analytics in the form of algorithms are imperfect and can only to a small extent capture the reasoning and analytical capabilities of people. For this reason, while big data/analytics can be useful, they are limited and must be used in conjunction with human knowledge and reasoning. Practical implications Snowden offers his views on big data/analytics and how they can be used effectively in real world situations in combination with human reasoning and input, for example in fields from resource management to individual health care. Originality/value Snowden is an innovative thinker. He combines knowledge and experience from many fields and offers original views and understanding of big data/analytics, knowledge and management.
      Citation: Journal of Knowledge Management
      PubDate: 2017-01-18T12:40:23Z
      DOI: 10.1108/JKM-08-2016-0330
  • Big data and knowledge management: a case of déjà vu or back to
           the future?
    • Abstract: Journal of Knowledge Management, Volume 21, Issue 1, February 2017.
      Purpose Big data clearly represents an important advance in information systems theory, but to describe it as ‘revolutionary’ is premature. Similar technological breakthroughs, from online databases to ERP, were clearly modulated by advances in the organizational domain, including matters of structure, strategy and culture and arguably big data will be similar. The purpose of this paper is to encourage discussion of the wider implications of big data for the theory and practice of knowledge management. Design/methodology/approach This is a conceptual study based on critical analysis of the relevant literatures including those of organizational studies and management, big data and knowledge management. Findings The literature of big data emphasises the application of algorithms to pattern analysis and prediction, resulting in data-driven decision making, with data being the creator of value in organizations and societies. This would appear to render obsolete previous depictions of the ‘data-information-knowledge’ relationship and in effect, spell the end of knowledge management. However, big data literature largely ignores the organizational dimension and significantly, the importance of frameworks, strategies and cultures for big data. As all of these are present in the literature of knowledge management, it would seem that big data has a long way to go to catch up and qualify even as a sub-discipline. Indeed, on the evidence, big data may well have a future as a contributor to and/or an element of knowledge management. Even for this to happen, however, major advances are required across the spectrum of big data technologies. Research limitations/implications This is a position paper written as the precursor for an empirical study. Originality/value The paper offers a critical literature-based and knowledge management perspective on big data while pointing out the common thread that runs through decades of advances in IS technologies.
      Citation: Journal of Knowledge Management
      PubDate: 2017-01-18T12:40:03Z
      DOI: 10.1108/JKM-07-2015-0277
  • Knowledge management and business performance: global experts’ views
           on future research needs
    • Pages: 1169 - 1198
      Abstract: Journal of Knowledge Management, Volume 20, Issue 6, Page 1169-1198, October 2016.
      Purpose This paper aims to examine the views of the global knowledge management (KM) community on the research area of KM and business performance and identify key future research themes. Design/methodology/approach An interview study spanning 222 informants in 38 countries was launched to collect data on KM expert views concerning the future research needs of the KM field. Findings The value contribution of KM requires more research despite experts agreeing on the complexities involved in solving this challenge. Further research areas identified were related to the influence of KM to support business strategy, intellectual capital, decision-making, knowledge sharing, organizational learning, innovation performance, productivity and competitive advantage. Research limitations/implications The sample is dominated by European-based KM experts and the self-selecting sampling approach that was used by relying on the networks of each partner could have biased the structure of this sample. Practical implications The recognition of the complexity to demonstrate the value contribution of KM could prevent practitioners from using over-simplified approaches and encourage them to use more advanced measurement approaches. Originality/value The paper is unique, in that it reports on the views of 222 KM experts from 38 countries representing both academia and practice, on the issue of future research needs in terms of KM and business outcomes. As such it provides valuable guidance for future studies in the KM field and related subjects.
      Citation: Journal of Knowledge Management
      PubDate: 2016-10-17T12:26:15Z
      DOI: 10.1108/JKM-12-2015-0521
  • Understanding counterproductive knowledge behavior: antecedents and
           consequences of intra-organizational knowledge hiding
    • Pages: 1199 - 1224
      Abstract: Journal of Knowledge Management, Volume 20, Issue 6, Page 1199-1224, October 2016.
      Purpose This paper aims to explore antecedents and consequences of intra-organizational knowledge hiding. Design/methodology/approach A model was developed and tested with data collected from 691 knowledge workers from 15 North American credit unions. Findings Knowledge hiding and knowledge sharing belong to unique yet possibly overlapping constructs. Individual employees believe that they engage in knowledge hiding to a lesser degree than their co-workers. The availability of knowledge management systems and knowledge policies has no impact on intra-organizational knowledge hiding. The existence of a positive organizational knowledge culture has a negative effect on intra-organizational knowledge hiding. In contrast, job insecurity motivates knowledge hiding. Employees may reciprocate negative knowledge behavior, and knowledge hiding promotes voluntary turnover. Practical implications Managers should realize the uniqueness of counterproductive knowledge behavior and develop proactive measures to reduce or eliminate it. Originality/value Counterproductive knowledge behavior is dramatically under-represented in knowledge management research, and this study attempts to fill that void.
      Citation: Journal of Knowledge Management
      PubDate: 2016-10-17T12:28:20Z
      DOI: 10.1108/JKM-05-2016-0203
  • What factors influence knowledge sharing in organizations? A social
           dilemma perspective of social media communication
    • Pages: 1225 - 1246
      Abstract: Journal of Knowledge Management, Volume 20, Issue 6, Page 1225-1246, October 2016.
      Purpose Enterprise social media platforms provide new ways of sharing knowledge and communicating within organizations to benefit from the social capital and valuable knowledge that employees have. Drawing on social dilemma and self-determination theory, the purpose of this paper is to understand what factors drive employees’ participation and what factors hamper their participation in enterprise social media. Design/methodology/approach Based on a literature review, a unified research model is derived integrating demographic, individual, organizational and technological factors that influence the motivation of employees to share knowledge. The model is tested using statistical methods on a sample of 114 respondents in Denmark. Qualitative data are used to elaborate and explain quantitative findings. Findings The findings pinpoint towards the general drivers and barriers to knowledge sharing within organizations. The significant drivers to knowledge sharing are: enjoy helping others, monetary rewards, management support, management encourages and motivates knowledge sharing behavior and knowledge sharing is recognized. The significant identified barriers are: change of behavior, lack of trust and lack of time. Practical implications The proposed knowledge sharing framework helps to understand what factors impact engagement on social media. Furthermore, the article suggests different types of interventions to overcome the social dilemma of knowledge sharing. Originality/value The study contributes to an understanding of factors leading to the success or failure of enterprise social media drawing on self-determination and social dilemma theory.
      Citation: Journal of Knowledge Management
      PubDate: 2016-10-17T12:30:39Z
      DOI: 10.1108/JKM-03-2016-0112
  • Exploration of multi-layered knowledge sharing participation: the roles of
           perceived benefits and costs
    • Pages: 1247 - 1267
      Abstract: Journal of Knowledge Management, Volume 20, Issue 6, Page 1247-1267, October 2016.
      Purpose This paper aims to explore participants’ perceived benefits and costs that influence the quantity and the quality of voluntary participation in knowledge networks in a resources-constrained economy. Design/methodology/approach A conceptual model of perceived benefits and costs of knowledge sharing is designed on the basis of literature. The influence of perceived benefit and cost on perceived quantity and quality of knowledge sharing are assessed on the basis of a survey with 283 participants in a business context within a resource-restrained economy. Findings The results indicate that reputation, reciprocity and altruism are perceived to benefit quantity of participation, whereas reciprocity, altruism and knowledge self-efficacy are perceived to benefit the quality of participation in knowledge networks. Effort and time have a negative impact on both quantity and quality of participation in knowledge sharing. Research limitations/implications This study provides insights into the factors that influence acceptance and use of knowledge networks and can thus influence business policies. Originality/value This exploratory study explores both perceived benefits and costs of participation in knowledge sharing in a resource-constrained economy.
      Citation: Journal of Knowledge Management
      PubDate: 2016-10-17T12:29:43Z
      DOI: 10.1108/JKM-01-2016-0044
  • Effects of knowledge management on client-vendor relationship quality: the
           mediating role of global mindset
    • Pages: 1268 - 1281
      Abstract: Journal of Knowledge Management, Volume 20, Issue 6, Page 1268-1281, October 2016.
      Purpose This study argues that knowledge management (KM) by itself has only limited effects on client–vendor relationship (CVR) of global providers of highly customised services. Rather, it is the ability of top management to properly evaluate and utilise a vast array of complex knowledge which allows global firms to develop and maintain superior CVR. The paper tests the proposition that global mindset (GM) of top management mediates the effects of KM on CVR quality. Design/methodology/approach The paper uses survey data from a sample of 68 international service providers (ISPs) in the information technology sector in India and partial least squares approach to structural equation modelling to test the hypotheses. Findings The results show that both KM and GM have positive and statistically significant effects on the quality of CVRs. The results also confirm that the GM of top management has significant and substantive mediation effects on the relationship between KM and CVR quality. Research limitations/implications The small size of the sample and the focus on ISPs in a single country constitute the main limitations of the study. Future research should ideally draw from a larger sample of ISPs from multiple countries and sectors in order to allow for greater generalisation of the findings. Practical implications ISPs will benefit from developing the GM of their top management teams to enhance their CVRs. Originality/value The paper provides new insights into how, in an international context, firms can transform their KM into superior CVR quality through the development of GM.
      Citation: Journal of Knowledge Management
      PubDate: 2016-10-17T12:31:11Z
      DOI: 10.1108/JKM-03-2016-0099
  • Intrinsic motivation for knowledge sharing – competitive intelligence
           process in a telecom company
    • Pages: 1282 - 1301
      Abstract: Journal of Knowledge Management, Volume 20, Issue 6, Page 1282-1301, October 2016.
      Purpose Knowledge about competitive environments is a determinant factor for the success of a firm, as it may allow it to anticipate threats and opportunities in its market. This study aims to explore variables that enable or prevent an employee’s intrinsic motivation to share knowledge. It studies the collection and sharing of information that may be a signal of future competitive moves in competitive intelligence (CI) processes. Design/methodology/approach Canonical correlation was used by utilizing survey data from a company. The study was based on the self-determination theory relating intrinsic motivation to behavior. Findings The study confirms the importance of different aspects motivating knowledge sharing behavior, such as information system’s support, top management support and information feed-back. Research limitations/implications The study is limited to one company, respecting the limitations of a case study, but external validation was impossible to test. Findings showed a strong correlation of some variables with intrinsic motivation and are coherent with other studies in the knowledge sharing field. Practical implications Firms introducing knowledge sharing processes should pay attention to the importance of information system support. The relationship with people involved is also important, as in supporting their collaborations and giving feed-back to contributions. Sustaining intrinsic motivation seems a fundamental aspect to the process’ success. Originality/value The study indicates the relation of different variables of motivation with motivation. It explores knowledge sharing in a CI process, an important process in firms nowadays. It shows important aspects that ensure continuity of knowledge sharing as informational feed-back and top management support. Canonical correlation was also used, a technique not frequently explored and useful to study correlation among groups of variables.
      Citation: Journal of Knowledge Management
      PubDate: 2016-10-17T12:27:13Z
      DOI: 10.1108/JKM-02-2016-0083
  • Role of knowledge brokers in communities of practice in Japan
    • Pages: 1302 - 1317
      Abstract: Journal of Knowledge Management, Volume 20, Issue 6, Page 1302-1317, October 2016.
      Purpose The purpose of this study is to investigate the role of knowledge brokers in Japanese communities of practice (CoP). This is because if knowledge brokers can connect across boundaries and introduce practices into another CoP, they can contribute by introducing practices as tacit knowledge to another CoP. Design/methodology/approach This study examines five hypotheses on knowledge brokers with respect to multi-membership in CoPs, knowledge brokering and career adaptability. In this study, an online questionnaire was administrated to 412 business persons, all employed by Japanese companies. Findings In line with the predictions, the results show that the cognition and behavior of multi-membership were composed of two factors: “creation and integration of diverse opinions” and “acceptance of diverse opinions”. With respect to covariance structure analysis, “concern”, one of the factors of career adaptability, had both direct and indirect effects on “knowledge brokering”. “Creation and integration of diverse opinions”, one of the factors of the cognition and behavior of multi-membership, had direct effects on “knowledge brokering”. Research limitations/implications Given that the data presented in this study are limited to knowledge brokers in Japanese CoPs, the study needs to be extended to an international context and to other kinds of knowledge brokers. Originality/value This study contributes to the findings which show the complexity of multi-membership and career adaptability. Upon closer examination, each subscale of multi-membership and career adaptability shows a different effect on knowledge brokering. In other words, this study reveals the importance of proactive behavior in integrating diverse opinions for knowledge brokering.
      Citation: Journal of Knowledge Management
      PubDate: 2016-10-17T12:31:02Z
      DOI: 10.1108/JKM-03-2016-0098
  • The impact of focus, function, and features of shared knowledge on re-use
           in emergency management social media
    • Pages: 1318 - 1332
      Abstract: Journal of Knowledge Management, Volume 20, Issue 6, Page 1318-1332, October 2016.
      Purpose The purpose of this study is to investigate how organizations use social media such as blogs to share and re-used knowledge during contingencies, disasters, and emergencies. The factors related to the knowledge itself – rather than the media – which lead to more and less re-use (particularly in the fast-paced and uncertain context of emergencies) are not well known. Design/methodology/approach Integrating theories of social media, knowledge management and mass communication, the author develops a model of the characteristics of knowledge (focus, function and features), characteristics of knowledge sharers and the user’s needs, which influence the extent to which knowledge is re-used. Findings A study of 645 blog posts revealed why some knowledge is re-used in emergencies more than other types of knowledge. Surprisingly, non-event-related knowledge is re-used more often than event-related knowledge, perhaps because users are less certain about how they would re-use non-event knowledge and, thus, are paradoxically more interested in what it might offer. Results also indicate several other factors which impact re-use. Practical implications Traditional mechanisms used to evaluate knowledge for reuse such as rank and organizational status are less important than the focus and function of the knowledge itself; they offer practitioners strategies for more efficient knowledge sharing during emergencies and identify opportunities for more effective employment of emergency management social media. Originality/value One of the first studies to dig deeper into factors of knowledge shared and re-used during emergencies, this research integrates several theoretical streams to explain why some knowledge is more valuable for re-use. It increases the understanding of knowledge sharing during disasters and offers strategies for development of knowledge systems for future emergencies.
      Citation: Journal of Knowledge Management
      PubDate: 2016-10-17T12:27:45Z
      DOI: 10.1108/JKM-04-2016-0177
  • Artifacts in knowledge management research: a systematic literature review
           and future research directions
    • Pages: 1333 - 1352
      Abstract: Journal of Knowledge Management, Volume 20, Issue 6, Page 1333-1352, October 2016.
      Purpose The purpose of this paper is to assess the role of artifacts in the knowledge management field in the past 18 years (1997-2015) and to identify directions for future research. Design/methodology/approach The authors conducted a systematic literature review of 101 articles published in seven journals retrieved from EBSCO and Google Scholar online research databases. The framework for analysis included 13 codes, i.e. author(s), title, year of publication, typology, theoretical lens, categorizations, methods for empirical work, relevancy, level of analysis, keywords, findings, research themes and future research directions. Codes were analyzed using qualitative and quantitative methods. Findings The findings lacked cumulativeness and consistency in the current knowledge management debate. Empirical works outnumbered conceptual contributions by two to one, and the majority of papers focused at the organizational level of analysis. Knowledge management systems, knowledge sharing and digital archives were the major research themes connected to artifacts, together with other closely aligned concepts such as learning and online learning, knowledge transfer and knowledge creation. Research limitations/implications This study has temporal and contextual limitations related to covered time span (18 years) and journals’ subscription restrictions. Originality/value This paper is a first attempt to systematically review the role of artifacts in knowledge management research and therefore it represents a primary reference in the knowledge management field. It provides directions to future theoretical and empirical studies and suggestions to managerial practices.
      Citation: Journal of Knowledge Management
      PubDate: 2016-10-17T12:30:06Z
      DOI: 10.1108/JKM-05-2016-0199
  • Old wine in new bottles: docility, attention scarcity and knowledge
    • Pages: 1353 - 1372
      Abstract: Journal of Knowledge Management, Volume 20, Issue 6, Page 1353-1372, October 2016.
      Purpose This paper aims to address the nature of docility in organizations, its practical role in attention scarcity and knowledge diffusion in complex organizations and the management implications for organizational learning and innovation to improve knowledge management. Design/methodology/approach This paper examines knowledge organizations from the perspective of human resource strategies, their role in information abundance and attention scarcity and techniques to enhance docility mechanisms at different levels of the organization to increase innovation and performance. Findings This paper, in reviewing the organization literature on attention scarcity, addresses the shortage of studies linking the need for docility – the desire to learn from workers and the desire to teach – in personnel practices of knowledge firms, where intense social interaction, social feedback and social learning are the norms. Practical implications Knowledge management – scanning, creation, coordination, interpreting, transfer and integration – may well be the basis of competitive advantage, based on human resource strategies to mobilize explicit and tacit knowledge via docility mechanisms, including mentoring, teamwork, coaching and deep collaboration. Originality/value Decades ago, Herbert A. Simon introduced this new concept, docility, which is now central to knowledge organizations that face information abundance and attention scarcity. Knowledge organizations require tools of docility to align human resource strategies to both strategic management and operational functions to enhance teaching and learning in design structures that are time-constrained.
      Citation: Journal of Knowledge Management
      PubDate: 2016-10-17T12:28:03Z
      DOI: 10.1108/JKM-03-2016-0124
  • The effects of knowledge management capabilities on perceived school
           effectiveness in career and technical education
    • Pages: 1373 - 1392
      Abstract: Journal of Knowledge Management, Volume 20, Issue 6, Page 1373-1392, October 2016.
      Purpose This study aims to investigate the impact that knowledge management (KM) capabilities have on school effectiveness in career and technical education (CTE) in Taiwan. Design/methodology/approach The study adopted a survey research. A total of 439 valid samples were obtained and subsequently verified with structural equation modeling. Findings The results indicated that KM capabilities consist of two main dimensions, namely, the KM enabler capabilities and the KM process capabilities. The former includes structures, cultures and information technology support, whereas the latter includes acquisitions, storage, sharing and applications. In terms of the relationships among the dimensions of the model structure, the KM enabler capabilities managed to effectively predict the KM process capabilities, and the KM process capabilities managed to effectively predict the perceived school effectiveness. Research limitations/implications Based on the results, improvement of the KM enabler capabilities and process capabilities of higher education institutions of CTE is recommended so that their school effectiveness may be improved. Because the participants were not randomly selected, the generalizability of the results should be further examined. Practical implications This study encourages practitioners to focus their KM practices on KM enabler capabilities and the KM process capabilities. Originality/value The current study provided an insight into and further understanding of the model regarding the relationships among the KM enabler capabilities, the KM process capabilities and the school effectiveness.
      Citation: Journal of Knowledge Management
      PubDate: 2016-10-17T12:27:32Z
      DOI: 10.1108/JKM-12-2015-0515
  • Tracing the historical origins of knowledge management issues through
           referenced publication years spectroscopy (RPYS)
    • Pages: 1393 - 1404
      Abstract: Journal of Knowledge Management, Volume 20, Issue 6, Page 1393-1404, October 2016.
      Purpose This study, using a new method called Referenced Publication Years Spectroscopy (RPYS), aims to examine the most important historic works written in the area of knowledge management (KM). Design/methodology/approach Preliminary data of this study have been extracted from Web of Science through scientometric methods. The references used in all the papers in the core journals in this field since 1980 to the end of 2014 were studied. Findings The distribution of resources in the area of KM based on the publication year indicates that this field of study, during time intervals 1900 to 1980, has seen eight major mutations. A considerable influence of such fields as economics, business, social networks analysis, organizational learning and economic sociology on the realm of KM is evident. The association of Polanyi with the mutations of 1958, 1962 and 1967 suggests his obvious influence on the evolution of KM. The ratio of articles to books among the whole documents detected by RPYS was 2-13 which could direct us to the point that the channel for information transformation in KM is more focused on books than on articles. Originality/value None of the few studies done by scientometric methods in the realm of KM has been seen through the issue of the historical origins of this area. This piece of research, using a new scientometric method, can be considered the first study in which the origins of KM over time have been studied.
      Citation: Journal of Knowledge Management
      PubDate: 2016-10-17T12:26:03Z
      DOI: 10.1108/JKM-01-2016-0019
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