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Journal Cover Journal of Knowledge Management
  [SJR: 1.12]   [H-I: 49]   [114 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1367-3270
   Published by Emerald Homepage  [335 journals]
  • Business analytics-enabled decision making effectiveness through knowledge
           absorptive capacity in health care
    • First page: 517
      Abstract: Journal of Knowledge Management, Volume 21, Issue 3, May 2017.
      Purpose Drawing on the resource-based theory and dynamic capability view, this study examines the mechanisms by which business analytics capabilities (i.e., the effective use of data aggregation, analytics, and data interpretation tools) in healthcare units indirectly influence decision-making effectiveness through the mediating role of knowledge absorptive capacity. Design/methodology/approach Using a survey method, this study collected data from the hospitals in Taiwan. Of the 155 responses received, three were incomplete, giving a 35.84% response rate with 152 valid data points. Structural equation modeling (SEM) was used to test the hypotheses. Findings This study conceptualizes, operationalizes, and measures the business analytics (BA) capability as a multi-dimensional construct that is formed by capturing the functionalities of BA systems in health care, leading to the conclusion that healthcare units are likely to obtain valuable knowledge through utilizing the data interpretation tools effectively. The effective use of data analysis and interpretation tools in healthcare units indirectly influence decision-making effectiveness, an impact that is mediated by absorptive capacity. Originality/value This study adds values to the literature by conceptualizing BA capabilities in healthcare and demonstrating how knowledge absorption matters when implementing BA to the decision making process. The mediating role of absorptive capacity not only provides a mechanism by which BA can contribute to decision making practices, but also offers a new solution to the puzzle of the IT productivity paradox in healthcare settings.
      Citation: Journal of Knowledge Management
      PubDate: 2017-03-21T12:19:43Z
      DOI: 10.1108/JKM-08-2015-0301
  • How MNC's subsidiaries may improve their innovative performance? The role
           of external sources and knowledge management capabilities.
    • First page: 540
      Abstract: Journal of Knowledge Management, Volume 21, Issue 3, May 2017.
      Purpose In this paper, we argue that firms that develop and possess superior knowledge management capabilities have the ability to better manage external knowledge and to combine them with internal one. Thus, this paper aims at exploring the effect of Knowledge Management (KM) practices on the relationship between external R&D and innovation performance. Design/methodology/approach We used a sample of 117 European MNC subsidiaries. An OLS regression analysis has been carried out in order to evaluate the moderator effect of knowledge management on the relationship between external R&D and innovative performance. Findings We found positive evidences in favor of a moderator effect of knowledge management. This means that subsidiaries with superior KM capabilities are more effective in using external R&D, augmenting the magnitude of their external sources of knowledge and, consequently, improving their innovative performance Practical implications Managerially speaking, both corporate and subsidiaries managers need to understand the relevance of managing knowledge effectively and efficiently at the subsidiary level. The first need to allocate more resources (both financial and managerial) to the subsidiaries that are active in knowledge transfer and sharing while the second need to implement practically knowledge management tools and processes at the subsidiary organizational level in order to improve subsidiary's innovative performance. Originality/value This paper contributes mainly to the Knowledge Management field highlighting the importance of knowledge management at the subsidiary level, whereas most of previous studies focus on different units of analysis.
      Citation: Journal of Knowledge Management
      PubDate: 2017-03-21T12:19:45Z
      DOI: 10.1108/JKM-09-2016-0411
  • On the path towards open innovation: Assessing the role of knowledge
           management capability and environmental dynamism in SMEs
    • First page: 553
      Abstract: Journal of Knowledge Management, Volume 21, Issue 3, May 2017.
      Purpose This study seeks to shed light on the internal and external antecedents of open innovation (OI) in the context of small and medium-sized enterprises (SMEs), with a special focus on the role of knowledge management (KM) capability. The paper develops and tests an integrative research model which assesses: 1) the effect of internal factors on KM capability; 2) the impact of organizational and external factors, namely KM capability and environmental dynamism, on OI; 3) and whether environmental dynamism moderates the relationship between KM capability and OI. Design/methodology/approach Drawing on the knowledge-based view and the social exchange and the contingency theories, this paper develops an integrative research model which analyzes several relations between organizational antecedents of KM capability and its effect on OI by using covariance-based structural equation modeling on a dataset of Spanish SMEs. Findings Results confirm that information technology-supported operations and commitment-based human resource practices have a positive and significant influence on KM capability. In contrast, results do not find support for the relationship between interdepartmental connectedness and KM capability, whereas both KM capability and environmental dynamism have a direct influence on OI. Originality/value This paper adds to existing research on OI as it is the first study that addresses the critical role of KM capability for the implementation of OI.
      Citation: Journal of Knowledge Management
      PubDate: 2017-03-21T12:19:55Z
      DOI: 10.1108/JKM-09-2016-0403
  • Reconfiguring the firm’s core technological portfolio through open
           innovation: focusing on technological M & A
    • First page: 571
      Abstract: Journal of Knowledge Management, Volume 21, Issue 3, May 2017.
      Purpose The purpose of this study is to investigate the effects of open innovation, especially focusing on technological M&A, on subsequent innovation and changes to the firm’s core technological portfolio. Design/methodology/approach The study suggests three types of core technological areas, based on prior focus and experience in technological categories. These are 1) the existing core area, in which the acquirer firm retains its knowledge and expertise, 2) the enhanced core area, where knowledge and expertise in the acquirer firm’s insufficient areas are strengthened, 3) the new core area, i.e., new knowledge fields in which the acquirer firm ventures into. The study then analyzes the effects of two key knowledge characteristics of the target firm, similarity and complementarity, on post-M&A innovation outcomes in each of the three core technological areas. Findings The results confirm that while none of the investigated knowledge characteristics of the target firm is advantageous for post-M&A innovation outcomes in existing core areas, similarity of the target firm does facilitate post-M&A innovation outcomes in enhanced core areas. Moreover, the results confirm that complementarity of the target firm is beneficial for post-M&A innovation outcomes in new core areas. Originality/value The study explains the reconfiguration mechanism of a firm’s core technological portfolio. It also suggests an extended framework to analyze innovation outcomes in more detail. Moreover, the study helps to explain why most M&As result in failure.
      Citation: Journal of Knowledge Management
      PubDate: 2017-03-21T12:19:46Z
      DOI: 10.1108/JKM-07-2016-0295
  • A two-sided matching decision method for supply and demand of
           technological knowledge
    • First page: 592
      Abstract: Journal of Knowledge Management, Volume 21, Issue 3, May 2017.
      Purpose The purpose is to propose a novel prospect-based two-sided matching decision model for matching supply and demand of technological knowledge assisted by a broker. This model enables the analyst to account for the stakeholders’ psychological behaviours and their impact on the matching decision in an open innovation setting. Design/methodology/approach The prospect theory and grey relational analysis are employed to develop the proposed two-sided matching decision framework. Findings By properly calibrating model parameters, the case study demonstrates that the proposed approach can be applied to real-world technological knowledge trading in a market for technology (MFT) and yields matching results that are more consistent with the reality. Research limitations/implications The proposed model does not differentiate the types of knowledge exchanged (established vs. novel, tacit vs. codified, general vs specialized) [Ardito et al. 2016, Nielsen and Nielsen 2009]. Moreover, the model focuses on incorporating psychological behaviour of the MFT participants and does not consider their other characteristics. Practical implications The proposed model can be applied to achieve a better matching between technological knowledge suppliers and users in a broker-assisted MFT. Originality/value This paper furnishes a novel theoretical model for matching supply and demand in a broker-assisted MFT. Methodologically, the proposed model can effectively capture market participants’ psychological considerations.
      Citation: Journal of Knowledge Management
      PubDate: 2017-03-21T12:19:44Z
      DOI: 10.1108/JKM-05-2016-0183
  • Teams and lead creators in cultural and creative industries: evidence from
           the Italian haute cuisine
    • First page: 607
      Abstract: Journal of Knowledge Management, Volume 21, Issue 3, May 2017.
      Purpose Into cultural and creative industries, the innovation is increasingly realized by a lead creator which is supported by a specific team. Hence, the present paper aims to understand the composition of this particular team. Design/methodology/approach We conducted an in-depth case study of “Dal Pescatore”. This is the Italian restaurant keeping the highest award previewed by Michelin Guide from the longer period of time. The main figures of the restaurant are the head-chefs (Nadia and Giovanni Santini) which are continually supported by a dedicated team Findings Our analysis underlines the necessity to create a team which combines aged people linked to firms’ tradition with a low percentage of young foreign apprentices. If the old-timers member assures a deep understanding of the firm’s knowledge base, the young foreign apprentice can show an high learning attitude through which he/she more easily shares their different knowledge. Research limitations/implications Our study discussed organizational efforts to foster innovation capacities of the main individuals into a firm. However, the present research suffers from some limitations which limits the generalizability of the results beyond the company studied: a single case study on a small and family firm with consolidated organizational routines. In addition, this research does not solutions about the mechanisms of interaction among these different team members. Originality/value Recent studies observed how a number of cultural and creative firms innovate through a particular team that develops the ideas of a lead creator. Nevertheless, despite the increasing importance of these teams, their composition remains unclear.
      Citation: Journal of Knowledge Management
      PubDate: 2017-03-21T12:19:42Z
      DOI: 10.1108/JKM-09-2016-0381
  • Big data investments in knowledge and non-knowledge intensive firms: what
           the market tells us
    • First page: 623
      Abstract: Journal of Knowledge Management, Volume 21, Issue 3, May 2017.
      Purpose This study investigates the value of big data investments by examining the market reaction to company announcements of big data investments and tests the effect for firms that are either knowledge intensive or not. Design/methodology/approach This study is based on an event study using data from two stock markets in China. Findings The stock market sees an overall index increase in stock prices when announcements of big data investments are revealed by grouping all the listed firms included in the sample. Increased stock prices are also the case for non-knowledge intensive firms. However, the stock market does not seem to react to big data investment announcements by testing the knowledge intensive firms along. Research limitations/implications This study contributes to the literature on assessing the economic value of big data investments from the perspective of big data information value chain by taking an unexpected change in stock price as the measure of the financial performance of the investment and by comparing market reactions between knowledge intensive firms and non-knowledge intensive firms. Findings of this study can be used to refine practitioners’ understanding of the economic value of big data investments to different firms and provide guidance to their future investments in knowledge management to maximize the benefits along the big data information value chain. However, findings of study should be interpreted carefully when applying them to companies that are not publicly traded on the stock market or listed on other financial markets. Originality/value Based on the concept of big data information value chains, this study advances research on the economic value of big data investments. Taking the perspective of stock market investors, this study investigates how the stock market reacts to big data investments by comparing the reactions to knowledge intensive firms and non-knowledge intensive firms. The results may be particularly interesting to those publicly traded companies that have not previously invested in knowledge management systems. The findings imply stock investors tend to believe that big data investment could possibly increase the future returns for non-knowledge intensive firms.
      Citation: Journal of Knowledge Management
      PubDate: 2017-03-21T12:19:47Z
      DOI: 10.1108/JKM-12-2016-0522
  • Knowledge driven preferences in informal inbound open innovation modes. An
           explorative view on small to medium enterprises.
    • First page: 640
      Abstract: Journal of Knowledge Management, Volume 21, Issue 3, May 2017.
      Purpose Through the lens of the open innovation model and knowledge-based view (KBV), the present research seeks to investigate three key factors (i.e. cognitive dimensions, the knowledge-driven approach and absorptive capacity) that are likely to determine the preference for informal inbound OI modes. The innovation literature has differentiated these collaborations into informal inbound open innovation (OI) entry modes and formal inbound OI modes, offering an advocative and conceptual view. However, empirical studies on these collaborations are still limited. Design/methodology/approach Building on the above theoretical framework, the empirical research was performed in two stages. First, data were collected via a closed-ended questionnaire distributed to all the participants from the sample by e-mail. Secondly, to assess the hypotheses structural equation modelling (SEM) via IBM® SPSS® Amos 20 was applied. Findings The empirical research was conducted on 175 small to medium enterprises in the United Kingdom, suggesting that the knowledge-driven approach is the strongest determinant leading to a preference for informal inbound OI modes. The findings were obtained using structural equation modelling (SEM) and are discussed in line with the theoretical framework. Research limitations/implications Due to the chosen context and sector of the empirical analysis, the research results may lack generalisability. Hence, new studies are proposed. Practical implications The paper includes implications for the development of informal inbound open innovation led by knowledge-driven approach. Originality/value This paper offers an empirical research to investigate knowledge-driven preferences in informal inbound open innovation modes.
      Citation: Journal of Knowledge Management
      PubDate: 2017-03-21T12:20:00Z
      DOI: 10.1108/JKM-10-2016-0465
  • Open innovation search in manufacturing firms: The role of organizational
           slack and absorptive capacity
    • First page: 656
      Abstract: Journal of Knowledge Management, Volume 21, Issue 3, May 2017.
      Purpose The purpose of this study is to explore organizational factors that act as antecedents of open innovation search. We aim to empirically examining whether the extent to which organizational slack is absorbed determines its influence on firms’ openness in innovation search. In addition, we also examine the moderating effect of absorptive capacity on the relationship between slack and open innovation search. Design/methodology/approach This study adopted secondary data from multiple sources (NBER, Compustat, and US census) and then constructed a 10-year balanced panel dataset of 298 manufacturers. The generalized least square method was used to explore the determinants of open innovation search among manufacturing firms. Findings The results of this study reveal that the absorption level of organizational slack indeed determines the openness in innovation search. Specifically, absorbed slack negatively affects a firm’s openness in innovation search, whereas unabsorbed slack promotes open innovation search. Additionally, the relationship between absorbed slack and open innovation search will be less negative with the increase of absorptive capacity. Originality/value Different from most previous studies that have examined the performance effect of open search among high-tech and large enterprises, this research focuses on the antecedents of open search strategy in both high- and low-tech large and small firms. The findings reveal that different forms of organizational slack divergently influence a firm’s open search strategy, contributing to the understanding of the relationship between organizational slack and knowledge search behavior in a broader context, as well as the understanding of the moderating effect of absorptive capacity.
      Citation: Journal of Knowledge Management
      PubDate: 2017-03-21T12:19:57Z
      DOI: 10.1108/JKM-09-2016-0368
  • Global ranking of knowledge management and intellectual capital academic
           journals: 2017 update
    • First page: 675
      Abstract: Journal of Knowledge Management, Volume 21, Issue 3, May 2017.
      Purpose The purpose of this study is to update a global ranking of twenty-seven knowledge management and intellectual capital (KM/IC) academic journals. Design/methodology/approach The ranking was developed based on a combination of results from a survey of 482 active KM/IC researchers and journal citation impact indices. Findings The ranking list includes 27 currently active KM/IC journals. The A+ journals are the Journal of Knowledge Management and the Journal of Intellectual Capital. The A journals are the Learning Organization, Knowledge Management Research & Practice, Knowledge and Process Management, VINE: The Journal of Information and Knowledge Management Systems, and International Journal of Knowledge Management. A majority of recently launched journals did not fare well in the ranking. Whereas a journal’s longevity is important, it is not the only factor affecting its ranking position. Expert survey and citation impact measures are relatively consistent, but expert survey ranking scores change faster. Practical implications KM/IC discipline stakeholders, including practitioners, editors, publishers, reviewers, researchers, students, administrators, and librarians, may consult the developed ranking list for various purposes. Compared to 2008, more researchers indicated KM/IC as their primary area of concentration, which is a positive indicator of discipline development. Originality/value This is the most recent ranking list of KM/IC academic journals.
      Citation: Journal of Knowledge Management
      PubDate: 2017-03-21T12:19:59Z
      DOI: 10.1108/JKM-11-2016-0490
  • Knowledge strategy planning: an integrated approach to manage uncertainty,
           turbulence, and dynamics
    • First page: 233
      Abstract: Journal of Knowledge Management, Volume 21, Issue 2, April 2017.
      Purpose Knowledge strategy and its planning are affected by uncertainty and environmental turbulence. This paper discusses these issues and presents knowledge strategy planning as an integrated approach for facing these conditions. Design/methodology/approach Based on an extensive survey and an original re-elaboration of the literature, the paper addresses these research questions: a) what is the meaning of knowledge strategy, and how can it be related to concepts such as strategic thinking, business strategy, and knowledge management in organizations? b) What are the limitations of pure rational approach to knowledge strategy in turbulent environments and under uncertainty? c) What approaches can be consequently proposed to formulate knowledge strategies? Findings The study provides a critical reading of the current literature. Also, it proposes an integrated approach that sees planning as a continuous effort of learning and adaptation to needs and opportunities that dynamically emerge from daily practices. Research limitations/implications The proposed framework can inspire a new research agenda to detect how knowledge strategies are planned in companies and how they are continuously adapted on the basis of a dialog between rational contributions and perceptions of reality, practical views, intuitions, and emotions. This can also inspire a new agenda for company strategists and KM professionals. Originality/value In the literature, little attention has been devoted to knowledge strategy planning. The paper contributes to fill this gap and proposes a new way to see knowledge strategy as an integration of rational thinking and dynamic learning.
      Citation: Journal of Knowledge Management
      PubDate: 2017-02-15T10:22:09Z
      DOI: 10.1108/JKM-02-2016-0071
  • Setting a knowledge boundary across teams: knowledge protection regulation
           for inter-team coordination and team performance
    • First page: 254
      Abstract: Journal of Knowledge Management, Volume 21, Issue 2, April 2017.
      Purpose As teams are built around specialized and different knowledge, they need to regulate their knowledge boundaries to exchange their specialized knowledge with other teams and to protect the value of such specialized knowledge. However, prior studies focus primarily on boundary spanning and imply that boundaries are obstacles to sharing knowledge. To fill this research gap, this study suggests the importance of knowledge protection regulation, an activity that sets an adequate boundary for protecting knowledge, and investigates the factors that facilitate knowledge protection regulation and its consequences. Design/methodology/approach This study collected empirical data from 196 teams in seven organizations. Through a validation of the measurement model, data from 138 teams are used for further analysis. The hypotheses effects are assessed using a structural equation model. Findings The analysis results indicate that both task uncertainty and task interdependency enhance knowledge protection regulation in teams, and that information technology support moderates the relationship between task uncertainty and knowledge protection regulation. The results also indicate that knowledge protection regulation improves inter-team coordination and team performance. Originality/value This study focuses on knowledge protection regulation through adopting communication privacy management theory in team level. The findings hint that boundary management is the process of communication, and the role of the teams’ nature on knowledge protection regulation. The findings also provide a new way to understand knowledge flow of the teams as well as the entire organization.
      Citation: Journal of Knowledge Management
      PubDate: 2017-02-15T10:22:17Z
      DOI: 10.1108/JKM-04-2016-0163
  • Managing extracted knowledge from big social media data for business
           decision making
    • First page: 275
      Abstract: Journal of Knowledge Management, Volume 21, Issue 2, April 2017.
      Purpose This paper propose a knowledge management framework for leveraging big social media data to help interested organizations integrate big data technology, social media and knowledge management systems to store, share and leverage their social media data. Specifically, this research focuses on extracting valuable knowledge on social media by contextually comparing social media knowledge among competitors. Design/methodology/approach A case study was conducted to analyze nearly one million twitter messages associated with five large companies in the retail industry (Costco, Walmart, Kmart, Kohl’s and The Home Depot) to extract and generate new knowledge and to derive business decisions from big social media data. Findings This case study confirms that our proposed framework is sensible and useful in terms of integrating big data technology, social media and knowledge management in a cohesive way to design a KM system and its process. Extracted knowledge is presented visually in a variety of ways to discover business intelligence. Originality/value Practical guidance for integrating big data, social media and knowledge management is scarce. This proposed framework is a pioneering effort in using big data technologies to extract valuable knowledge on social media and to discover business intelligence by contextually comparing social media knowledge among competitors.
      Citation: Journal of Knowledge Management
      PubDate: 2017-02-15T10:22:15Z
      DOI: 10.1108/JKM-07-2015-0296
  • Value generation from industry-science linkages in light of targeted open
    • First page: 295
      Abstract: Journal of Knowledge Management, Volume 21, Issue 2, April 2017.
      Purpose The article provides a substantial overview of features and channels of knowledge and technology transfer in light of achieving impact from science and research. Design/methodology/approach The article is conceptual with substantial desk research undertaken. A taxonomy of transfer channels is proved and levels of impact from STI proposed. Findings It’s found that there are different levels of value generated from STI, each featuring different stakeholders with different agendas and expectations. It’s argued that to make knowledge and technology transfer impactful and sustainable a long term and holistic view and approach is required. Originality/value Against most articles about technology and knowledge transfer this work presents an overarching overview of objects, channels and features of partners involved in transfer. It’s features technology and knowledge transfer from a holistic perspective and provides useful background for future empiric studies and impact assessments.
      Citation: Journal of Knowledge Management
      PubDate: 2017-02-15T10:22:11Z
      DOI: 10.1108/JKM-11-2016-0510
  • Knowledge management as a factor for the formulation and implementation of
           organization strategy
    • First page: 308
      Abstract: Journal of Knowledge Management, Volume 21, Issue 2, April 2017.
      Purpose Knowledge management (KM) and organization strategy are both important to the success of an organization. This study has looked for the research needs of their interrelationship. Design/methodology/approach The research is based on a collection of over 200 interviews of KM worldwide experts. Their inputs have been categorized based on the frequency of their occurrence. Findings This study looked at the research themes recommended by the experts and concluded that KM is to be regarded as a factor for the formulation and implementation of the organization strategy. Research limitations/implications The sample of scholars and practitioners interviewed, the analysis approach employed, and the use of broad questions and dimensions are some of the limitations of this study. Nevertheless, a variety of effects KM has on the formulation and implementation of company strategy has emerged. Practical implications Organizations would improve their chances of success in a changing and competitive world by integrating the KM approach, methods, and goals within the articulation of their strategy. Originality/value This study is original in the variety due to the wide demographic sample supplied, and to the approach both to KM academic experts as well as to practitioners. Its value is in the recommendations on the research of KM and organization strategy that would be of value, not only to organizations looking for ways to make their strategy more effective, but also to those willing to implement KM in a better way.
      Citation: Journal of Knowledge Management
      PubDate: 2017-02-15T10:21:30Z
      DOI: 10.1108/JKM-02-2016-0068
  • Citation classics published in Knowledge Management journals. part iii:
           author survey
    • First page: 330
      Abstract: Journal of Knowledge Management, Volume 21, Issue 2, April 2017.
      Purpose This paper is the third part of a series of works investigating the top 100 KM citation classic articles. Its purpose is to understand why KM citation classics are well-cited. Design/methodology/approach The results of a survey of 58 KM citation classic authors were reported as descriptive statistics and subjected to content analysis. Findings An archetype of a KM citation classic author was constructed including demographics, personal characteristics, motivation, and work preferences. There is a need for developing novel ideas in KM research. Timeliness of a publication is directly linked to its future impact. Editors should involve citation classics authors as reviewers, and KM researchers should improve their citation practices. Serendipity played a very important role in early KM research, especially from the perspective of discovering new and interesting phenomena. Research limitations/implications Whereas the importance of serendipity is not questioned, future KM researchers should rely more on a formal, meticulous, and well-planned research approach rather than on the hope of making a discovery by accident or luck. KM citation classics authors relied on serendipity to form the foundation of the discipline, but extending their work requires formal and structured inquiries. Practical implications Many authors did research to solve a problem to serve the needs of both practice and academia, rather than being overly theoretical. Originality/value Because KM researchers can no longer rely on past bibliometric theories, this paper helps understand why specific articles are highly cited and recommends how to conduct and develop future KM research that has impact.
      Citation: Journal of Knowledge Management
      PubDate: 2017-02-15T10:22:13Z
      DOI: 10.1108/JKM-07-2016-0300
  • Knowledge management, problem solving and performance in top Italian firms
    • First page: 355
      Abstract: Journal of Knowledge Management, Volume 21, Issue 2, April 2017.
      Purpose The objective of this paper is to empirically test the link among knowledge management practices, problem solving processes, and organizational performance. Design/methodology/approach This study uses survey data from 112 leading Italian companies. To test the structural relations of the research model we used the partial least square (PLS) method. Findings Results show a strong relationship between knowledge management practices and intermediate activities of creative problem solving and problem solving speed. In addition, creative problem solving has a direct impact on both organizational and financial performances while problem solving speed has a direct effect only on financial performance. Research limitations/implications The focus on top Italian firms limits the generalizability of results. Practical implications This study provides empirical evidence of the importance of knowledge management practices for problem solving activities and firm’s performance. Originality/value The present paper fills an important gap in the extant literature by conceptualizing and empirically testing the relationship among knowledge management, problem solving processes (creative problem solving and problem solving speed) and firm performance. This study is the first ever to study these relationships within the Italian context.
      Citation: Journal of Knowledge Management
      PubDate: 2017-02-15T10:22:16Z
      DOI: 10.1108/JKM-03-2016-0113
  • Knowledge management activities in social enterprises: lessons for small
           and non-profit firms
    • First page: 376
      Abstract: Journal of Knowledge Management, Volume 21, Issue 2, April 2017.
      Purpose This paper explores what Social Enterprises (SEs) in the UK know and how they acquire, convert, apply and protect this knowledge. This will enable them to manage their knowledge effectively, hence improve their practices and maximize the creation of social, environmental and economic value. Design/methodology/approach This study follows a qualitative approach, comprising of 21 interviews with founders and senior members of SEs in UK. Findings The results show that the investigated SEs have KM practices similar to the already identified in SMEs, associated with informality, reliance on external sources and focus on socialisation activities, but they have unique challenges on managing their knowledge related to their hybrid mission, to include social and economic objectives, and their closed relationship with stakeholders. Research limitations/implications As there is limited research on Knowledge Management (KM) practices in SEs; they were defined based on previous studies in large, private and public companies. Therefore, not all practices may be included. This research is a starting point in the study of KM in SEs. Practical implications This study identifies knowledge activities that enable the creation of social, environmental and economic value in SEs. This allows SEs, small firms and non-profit organisations to review their current practices and develop plans for their further improvement. Originality/value This paper is one of the first empirical studies exploring KM practices in SEs, highlighting their informal nature as well as their impact in and on the enterprise.
      Citation: Journal of Knowledge Management
      PubDate: 2017-02-15T10:21:35Z
      DOI: 10.1108/JKM-01-2016-0026
  • To gain or not to lose? The effect of monetary reward on motivation
           and knowledge contribution
    • First page: 397
      Abstract: Journal of Knowledge Management, Volume 21, Issue 2, April 2017.
      Purpose To better harness knowledge resources within and beyond organizations, firms are spending millions on building knowledge management systems and firm-hosted online communities. Individual knowledge contribution, which determines the effectiveness of information systems, benefits the organization at the cost of individual advantage as knowledge is usually considered highly private or even a source of individual prestige. Therefore, organizations provide rewards to compensate for their contributors’ knowledge loss. Surprisingly, some scholars report a positive relationship between reward and knowledge contribution, while others find this relationship to be insignificant or even negative. Based on regulatory focus theory, this study proposes and tests that such inconsistencies result from disparity between reward type and knowledge contribution measures. Design/methodology/approach A between-group laboratory experiment with 144 undergraduate student is designed and hierarchical regression is applied to test the hypotheses. Findings An incremental reward (additional reward for attaining outstanding achievements) aroused individual promotion focus, leading to an increase in self-perceived knowledge contribution (self-reported) and knowledge contribution quantity (experiment observers rated), but a decrease in knowledge contribution quality (peer rated). However, a decremental reward (deducted for errors) primed individual prevention focus, leading to an increase in self-perceived knowledge contribution (self-reported) and knowledge contribution quality (peer rated), but a decrease in knowledge contribution quantity (experiment observers rated). Originality/value The findings help explain why previous empirical results on the reward-knowledge contribution relationship were inconsistent and add to extant literature by introducing a new theoretical perspective for understanding motivation in knowledge management research.
      Citation: Journal of Knowledge Management
      PubDate: 2017-02-15T10:21:33Z
      DOI: 10.1108/JKM-03-2016-0100
  • How gamification of an enterprise collaboration system increases knowledge
           contribution: an affordance approach
    • First page: 416
      Abstract: Journal of Knowledge Management, Volume 21, Issue 2, April 2017.
      Purpose This study examines how gamification increases employees’ knowledge contribution to the place of work. It develops and tests the conjecture that gamification adds hedonic value to the use of an enterprise collaboration system (ECS), which, in turn, increases in both the quality and quantity of knowledge contribution. Design/methodology/approach Drawing on the framework of successful gamification against a backdrop of affordance theory, this study develops and tests a theoretical model that explains the effects of gamification affordances on knowledge contribution via the use of an ECS. Empirical data were gathered from 166 employees at a global company that used a gamified ECS designed to aid knowledge sharing. Findings Results using structural equation modeling showed that three gamification affordances—rewardability, competition, and visibility of achievement—jointly influenced employees’ perceived hedonic value of the ECS, which, in turn, increased knowledge contribution. Practical implications The results indicate that designing affordances that can increase hedonic value is central to facilitating employees’ knowledge contribution. However, simply incorporating game artifacts does not guarantee increased hedonic value of an ECS. Instead, assessing, monitoring, and diagnosing what affordances users perceive from the use of a gamified system are important. Originality/value By conceptualizing gamification affordances rather than specifying the design features of enterprise applications, this study provides meaningful insights into how the benefits of gamification can be harnessed for knowledge management in organizations.
      Citation: Journal of Knowledge Management
      PubDate: 2017-02-15T10:21:25Z
      DOI: 10.1108/JKM-10-2016-0429
  • Employees’ online knowledge sharing: the effects of
           person-environment fit
    • First page: 432
      Abstract: Journal of Knowledge Management, Volume 21, Issue 2, April 2017.
      Purpose Various environmental and individual factors influencing employees’ online knowledge sharing have been identified but our understanding has mostly been limited to their independent and direct effects. This study proposes that the fit between employees and their environments (PE fit) matters. A model explaining how PE fit and misfit affect employees’ knowledge sharing behavior through influencing their affective commitment is proposed and assessed. Design/methodology/approach The proposed model was assessed with data collected in a survey of 218 employees. Findings Results indicate that PE fit in the norm of collaboration, innovativeness, and skill variety leads to the development of stronger affective commitment and therefore more knowledge sharing behavior than when they are in shortfall or excess in the environment (i.e., PE misfit). Originality/value The findings indicate a new direction for knowledge sharing research that focuses on PE fit and suggest that knowledge sharing can be improved more proactively in practice by assessing PE fit during recruitment.
      Citation: Journal of Knowledge Management
      PubDate: 2017-02-15T10:21:34Z
      DOI: 10.1108/JKM-10-2016-0437
  • Means-ends based know-how mapping
    • First page: 454
      Abstract: Journal of Knowledge Management, Volume 21, Issue 2, April 2017.
      Purpose The paper reports on research that aims to make knowledge and in particular know-how more easily accessible to both academic and industrial communities, as well as to the general public. The paper proposes a novel approach to map out know-how information, so all knowledge stakeholders are able to contribute to the knowledge and expertise accumulation, as well as using that knowledge to research and apply expertise to address problems. Design/methodology/approach This research followed a design science approach in which mapping of know-how was done by the research team and then tested with graduate students. During this research the mapping approach was continuously evaluated and refined, and mapping guidelines and a prototype tool were developed. Findings Following an evaluation with graduate students, we found that the know-how maps we produced were easy to follow, allowed for continuous evolution, facilitated easy modification through provided modularity capabilities, further supported reasoning about know-how, and overall provide adequate expressiveness. Furthermore, we applied t Practical implications This paper argues that mapping out know-how within research and industry communities can further improve resource (knowledge) utilization, reduce the phenomena of “re-inventing the wheel”, and further create linkage across communities. Originality/value Having the qualities mentioned above, know-maps can both ease and support the increase of access to expert knowledge to various communities, and thus promoting re-use and expansion of knowledge for various purposes. Having an explicit representation of know-how further encourages innovation, as knowledge from various domain can be mapped, searched, and reasoned about, gaps can be identified and filled.
      Citation: Journal of Knowledge Management
      PubDate: 2017-02-15T10:21:31Z
      DOI: 10.1108/JKM-04-2016-0173
  • Empowering group leaders encourages knowledge sharing: integrating the
           social exchange theory and positive organizational behavior perspective
    • First page: 474
      Abstract: Journal of Knowledge Management, Volume 21, Issue 2, April 2017.
      Purpose Knowledge sharing usually happens in a work group context, but we rarely know how group leaders influence their members’ knowledge-sharing performance. Based on social exchange theory (SET) and the perspective of positive organizational behavior (POB), this study argues that a group leader’s positive leadership (e.g., empowering leadership) can help group members develop positive psychological capital, which can increase their knowledge sharing. Design/methodology/approach We conduct a multilevel analysis to explore the interrelationship among empowering leadership, psychological capital, and knowledge sharing. The sample includes 64 work groups consisting of 537 group members, and empirical testing is carried out by hierarchical linear modeling. Findings The results show that empowering leadership in a work group has a direct cross-level impact on members’ knowledge sharing and that psychological capital partially mediates the relationship between empowering leadership and knowledge sharing. As a result, this study shows that group leaders with positive leadership can help their members develop better positive psychological resources, which should lead to better knowledge sharing. Originality/value Based on the multi-level perspective and SET, this is the first study to explore how group leaders’ empowering leadership influences on members’knowledge sharing. Depending on integrating the POB perspective into SET, this study also the first one that connects two emerging and important research issues- POB and knowledge sharing.
      Citation: Journal of Knowledge Management
      PubDate: 2017-02-15T10:22:12Z
      DOI: 10.1108/JKM-08-2016-0318
  • Looking beyond knowledge sharing: an integrative approach to knowledge
           management culture
    • First page: 492
      Abstract: Journal of Knowledge Management, Volume 21, Issue 2, April 2017.
      Purpose A diverse range of knowledge processes have been referred to in the extant literature, but little agreement exists on which knowledge processes are critical and should be supported by organizational culture. This study is designed to identify the main knowledge processes associated with organizational knowledge culture. Design/methodology/approach Using a systematic literature review methodology, this study examined the primary literature – peer reviewed and scholarly articles published in the top seven knowledge management and intellectual capital (KM/IC) related journals. Findings The core knowledge processes have been identified – knowledge sharing, knowledge creation and knowledge implementation. The paper suggests that a strategy for implementing successful organizational knowledge management initiatives requires precise understanding and effective management of the core knowledge infrastructures and processes. Although technology infrastructure is an important aspect of any knowledge management initiative, the integration of knowledge into management decisions and practices relies on the extent to which the organizational culture supports or hinders knowledge processes. Research limitations/implications The focus of the study was on the articles published in the top seven KM/IC journals, important contributions in relevant publications in other KM journals, conference papers, books and professional reports may have been excluded. Practical implications Practitioners will benefit from a better understanding of knowledge processes involved in knowledge management initiatives and investments. From a managerial perspective, the study offers an overview of the state of organizational knowledge culture research and suggests that for KM initiatives to be successful, the organization requires an integrated culture that is concerned with knowledge processes as a set of inextricably inter-related processes. Originality/value For the first time a comprehensive list of diverse terms used in describing knowledge processes has been identified. The findings remove the conceptual ambiguity resulting from the inconsistent use of different terms for the same knowledge process by identifying the three major and overarching knowledge processes. Moreover, this study points to the need to attend to the inextricably interrelated nature of these three knowledge processes. Finally, this is the first time that a study provides evidence that shows the KM studies appear to be biased towards Knowledge Sharing.
      Citation: Journal of Knowledge Management
      PubDate: 2017-02-22T06:08:45Z
      DOI: 10.1108/JKM-06-2016-0216
  • Does big data mean big knowledge? KM perspectives on big data and
    • Pages: 1 - 6
      Abstract: Journal of Knowledge Management, Volume 21, Issue 1, Page 1-6, February 2017.
      Purpose This viewpoint study aims to make 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 study expresses the opinions of the guest editors of “Does Big Data Mean Big Knowledge? Knowledge Management 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 academics 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 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: 2017-03-07T10:54:28Z
      DOI: 10.1108/JKM-08-2016-0339
  • Davenport and Prusak on KM and big data/analytics: interview with David J.
    • Pages: 7 - 11
      Abstract: Journal of Knowledge Management, Volume 21, Issue 1, Page 7-11, February 2017.
      Purpose Larry Prusak and Tom Davenport have long been leading voices in the knowledge management (KM) field. This interview aims to explore 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. 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: 2017-03-07T10:53:23Z
      DOI: 10.1108/JKM-08-2016-0329
  • Dave Snowden on KM and big data/analytics: interview with David J. Pauleen
    • Pages: 12 - 17
      Abstract: Journal of Knowledge Management, Volume 21, Issue 1, Page 12-17, 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: 2017-03-07T10:53:11Z
      DOI: 10.1108/JKM-08-2016-0330
  • Big data text analytics: an enabler of knowledge management
    • Pages: 18 - 34
      Abstract: Journal of Knowledge Management, Volume 21, Issue 1, Page 18-34, February 2017.
      Purpose The purpose of this paper is to examine the role of big data text analytics as an enabler of knowledge management (KM). The paper argues that big data text analytics represents an important means to visualise and analyse data, especially unstructured data, which have the potential to improve KM within organisations. Design/methodology/approach The study uses text analytics to review 196 articles published in two of the leading KM journals – Journal of Knowledge Management and 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 have a key enabler role in KM. Drawing on the 196 articles analysed, the paper shows the power of big data-oriented text analytics tools in supporting KM through the visualisation of data. In this way, the authors highlight the nature and quality of the knowledge generated through this method for efficient KM in developing a competitive advantage. Research limitations/implications The research has important implications concerning the role of big data text analytics in KM, and specifically the nature and quality of knowledge produced using text analytics. The authors use text analytics to exemplify the value of big data in the context of KM 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 a means to enhance the effectiveness of KM. The paper provides important insights that can be applied to different business functions, from supply chain management to marketing management to support KM, 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 KM.
      Citation: Journal of Knowledge Management
      PubDate: 2017-03-07T10:53:34Z
      DOI: 10.1108/JKM-06-2015-0238
  • An exploration of contemporary organizational artifacts and routines in a
           sustainable excellence context
    • Pages: 35 - 56
      Abstract: Journal of Knowledge Management, Volume 21, Issue 1, Page 35-56, 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 purpose 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: 2017-03-07T10:53:13Z
      DOI: 10.1108/JKM-10-2015-0366
  • How the Internet of Things can help knowledge management: a case study
           from the automotive domain
    • Pages: 57 - 70
      Abstract: Journal of Knowledge Management, Volume 21, Issue 1, Page 57-70, February 2017.
      Purpose Current knowledge management (KM) systems cannot be used effectively for decision-making because of the lack of real-time data. This study aims to discuss how 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 This 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 The literature review shows that there are few papers discussing how KM can benefit by embedding IoT and processing incoming big data collected from IoT devices. The case study developed in this study 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: 2017-03-07T10:53:59Z
      DOI: 10.1108/JKM-07-2015-0291
  • Information and reformation in KM systems: big data and strategic
    • Pages: 71 - 91
      Abstract: Journal of Knowledge Management, Volume 21, Issue 1, Page 71-91, February 2017.
      Purpose The purpose of this paper is to provide a theoretical framework of how knowledge management (KM) systems can facilitate the incorporation of big data into strategic decisions. 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 KM systems. Design/methodology/approach To address this gap, a combined approach has been applied. The KM and data analysis systems implemented by companies were analyzed, and the analysis was complemented by a review of the extant literature. Findings Four types of data-based decisions and a set of ground rules are identified toward 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 to facilitate the incorporation of big data and advanced analytics into organizations’ strategic decision-making. Originality/value This is the first typology of data-based decision-making considering advanced analytics.
      Citation: Journal of Knowledge Management
      PubDate: 2017-03-07T10:54:18Z
      DOI: 10.1108/JKM-07-2015-0293
  • Big data systems: knowledge transfer or intelligence insights?
    • Pages: 92 - 112
      Abstract: Journal of Knowledge Management, Volume 21, Issue 1, Page 92-112, February 2017.
      Purpose This paper aims to bring together the existing theory from knowledge management (KM), competitive intelligence (CI) and big data analytics to develop a more comprehensive view of the full range of intangible assets (data, information, knowledge and 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 to illustrate how one can identify and understand what these different circumstances might look like. Design/methodology/approach The approach is chiefly conceptual, combining theory from multiple disciplines enhanced with practical applications. Illustrative data drawn from other empirical work are applied to illustrate some concepts. Findings Theory suggests that the KM 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, the CI theory might be a 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 KM and analytics/intelligence functions will help all intangibles systems to work more effectively. Originality/value To the authors’ 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: 2017-03-07T10:53:47Z
      DOI: 10.1108/JKM-07-2015-0300
  • Big data and knowledge management: a case of déjà vu or back to
           the future?
    • Pages: 113 - 131
      Abstract: Journal of Knowledge Management, Volume 21, Issue 1, Page 113-131, February 2017.
      Purpose Big data clearly represent 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 emphasizes 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 have 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 information systems technologies.
      Citation: Journal of Knowledge Management
      PubDate: 2017-03-07T10:52:58Z
      DOI: 10.1108/JKM-07-2015-0277
  • Creation of knowledge-added concept maps: time augmention via pairwise
           temporal analysis
    • Pages: 132 - 155
      Abstract: Journal of Knowledge Management, Volume 21, Issue 1, Page 132-155, 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 – designed to use objective quantitative pair-wise temporal operators to automatically detect co-occurring hot concepts. This PTA approach is demonstrated and validated without loss of generality for a spectrum of information technologies. Findings The rigorous validation study shows that the resulting temporal assessments are highly correlated with subjective assessments of experts (n = 136), exhibiting substantial reliability-of-agreement measures and average predictive validity above 85 per cent. Practical implications Using massive amounts of textual documents available on the Web to first generate a concept map 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 a text-based concept map (i.e. semantic graph) via detection and representation of temporal relationships. The originality and value of the proposed method is highlighted in comparison to other knowledge discovery methods.
      Citation: Journal of Knowledge Management
      PubDate: 2017-03-07T10:53:25Z
      DOI: 10.1108/JKM-07-2016-0279
  • Facilitating knowledge management through filtered big data: SME
           competitiveness in an agri-food sector
    • Pages: 156 - 179
      Abstract: Journal of Knowledge Management, Volume 21, Issue 1, Page 156-179, February 2017.
      Purpose This paper aims to critique a facilitated knowledge management (KM) process that utilises filtered big data and, specifically, the process effectiveness in overcoming barriers to small and medium-sized enterprises’ (SMEs’) use of big data, the processes enablement of SME engagement with and use of big data and the process 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 KM processes are implemented. Several barriers to knowledge acquisition and application stem from SME resource limitations, strategic orientation and asymmetrical power relationships within a 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 data set 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 data set. 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: 2017-03-07T10:54:07Z
      DOI: 10.1108/JKM-08-2016-0357
  • Interrelationship between big data and knowledge management: an
           exploratory study in the oil and gas sector
    • Pages: 180 - 196
      Abstract: Journal of Knowledge Management, Volume 21, Issue 1, Page 180-196, February 2017.
      Purpose The purpose of this paper 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 nine semi-structured interviews with open-ended and probing questions. Findings Useful predictive knowledge can be generated through big data to help companies improve their KM 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 generalizability. Originality/value This paper fulfills an identified need of exploring the relationship between big data and KM which has not been discussed much in the literature.
      Citation: Journal of Knowledge Management
      PubDate: 2017-03-07T10:53:16Z
      DOI: 10.1108/JKM-07-2016-0262
  • Cognitive big data: survey and review on big data research and its
           implications. What is really “new” in big data?
    • Pages: 197 - 212
      Abstract: Journal of Knowledge Management, Volume 21, Issue 1, Page 197-212, February 2017.
      Purpose The purpose of this paper is to introduce and define Cognitive Big Data as a concept. Furthermore, it investigates what is really “new” in Big Data, as it seems to be a hyped-up concept that has emerged during recent years. The purpose is also to broaden the discussion around Big Data far beyond the common 4V (velocity, volume, veracity and variety) model. Design/methodology/approach The authors 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 analyzing over 60 literature resources. They identified typical baseline scenarios (traffic, business processes, retail, health and social media) as a starting point from which they explored the notion of Big Data from different perspectives. Findings They concluded that the idea of Big Data is simply not new and recognized the need to re-think a new approach toward Big Data. The authors also introduced a five-Trait Framework for “Cognitive Big Data”, socio-technical system, data space, data richness, knowledge management (KM)/decision-making and visualization/sensory presentation. Research limitations/implications The viewpoint is centered on cognitive processes as KM process. Practical implications Data need 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. The authors need to know about data that can be ignored, data that they are not aware of (dark data) and data that can be fully utilized for analysis (light data). In the foreground is the extension of human mental capabilities and data understandability. Social implications Cognitive Big Data implies a socio-technological knowledge system. Originality/value Introduction of cognitive Big Data as concept and framework.
      Citation: Journal of Knowledge Management
      PubDate: 2017-03-07T10:53:56Z
      DOI: 10.1108/JKM-07-2016-0307
  • The concepts of big data applied in personal knowledge management
    • Pages: 213 - 230
      Abstract: Journal of Knowledge Management, Volume 21, Issue 1, Page 213-230, February 2017.
      Purpose The purpose of this paper is to demonstrate the applications of big data in personal knowledge management (PKM). Design/methodology/approach Five conventional knowledge management dimensions, namely, the value of data, data collection, data storage, data application and data presentation, were applied for integrating big data in the context of PKM. 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 PKM. Originality/value While the literature discusses PKM without considering the impact of big data, this paper aims to extend existing knowledge by demonstrating the application of big data in PKM.
      Citation: Journal of Knowledge Management
      PubDate: 2017-03-07T10:54:33Z
      DOI: 10.1108/JKM-07-2015-0298
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