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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.

A face-to-face interview was held with Dave Snowden in May 2015 in Auckland, New Zealand.

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.

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.

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.

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.

An interview was conducted by email with Larry Prusak and Tom Davenport in 2015 and updated in 2016.

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.

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.

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.

This study expresses the opinions of the guest editors of “Does Big Data Mean Big Knowledge? Knowledge Management Perspectives on Big Data and Analytics”.

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.

This is an opinion piece, and the proposed model still needs to be empirically verified.

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.

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.

This paper reports on the results of a qualitative study of middle managers’ perceptions of knowledge management (KM) implementation in NZ organizations. Data were collected in a survey of 71 attendees of a KM presentation. The data were analyzed using qualitative coding principles. Two core issues were examined – barriers and drivers of KM. Subcategories under barriers were primarily concerned with factors internal to the organization such as organizational culture, leadership, and education. Drivers were mostly external to the organization and included competition, peer pressure, and the need for increased productivity. The results indicate that the way managers manage themselves and their organizations are perceived to be the biggest barriers to KM implementation.

This paper aims to investigate 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.

This study is based on an event study using data from two stock markets in China.

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.

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.

Based on the concept of big data information value chain, 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 that stock investors tend to believe that big data investment could possibly increase the future returns for non-knowledge-intensive firms.

This study aims to identify the main knowledge processes associated with organizational knowledge culture. 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.

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.

The core knowledge processes have been identified – knowledge sharing, knowledge creation and knowledge implementation. The paper suggests that a strategy for implementing successful organizational KM initiatives requires precise understanding and effective management of the core knowledge infrastructures and processes. Although technology infrastructure is an important aspect of any KM initiative, the integration of knowledge into management decisions and practices relies on the extent to which the organizational culture supports or hinders knowledge processes.

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.

Practitioners will benefit from a better understanding of knowledge processes involved in KM 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.

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.

The outbreak of COVID-19 is one of the most serious health events in recent times. In the business landscape, its effects may be more detrimental to micro-, small- and medium-sized enterprises (MSMEs) because they tend to have limited financial and human resources to manage the challenges caused by COVID-19. To help MSMEs enhance their resilience, this paper aims to discuss how they can leverage mass collaboration to build social media-based knowledge ecosystems to manage interactions among internal and external stakeholders for knowledge creation and innovation.

The paper proposes a model for MSMEs to build an online knowledge ecosystem and a standalone text analytics tool to use the advanced data analytics, e.g. topic modeling, to analyze and aggregate collective insights. Design science research methodology is used to develop the model and the tool.

Through mass collaboration using social media and advanced data analytics technology, MSMEs can generate new business ideas, leading to enhanced resilience to meet the challenges caused by COVID-19 or other unexpected or extraordinary circumstances, such as natural disasters and financial crises.

To the best of authors’ knowledge, this paper is one of the first papers in social media adoption for knowledge creation and innovation research, providing detailed approaches for MSMEs to build a knowledge ecosystem on social media and to use advanced data analytics to mine the meaning of the generated data.

The purpose of this paper is to examine the factors that influence knowledge processes and by extension organisational knowledge culture (KC).

Using a systematic model development approach based on an extensive literature review, the authors explore the notion of organisational KC and conceptualise a model that addresses the following research question: what factors affect employees’ values and beliefs about knowledge processes and by extension organisational KC?

This paper proposes that knowledge processes are interrelated and mutually enforcing activities, and that employee perceptions of various individual, group and organisational factors underpin employee values and beliefs about knowledge processes and help shape an organisation’s KC.

The findings extend the understanding of the concept of KC and may point the way towards a unifying theory of knowledge management (KM) that can better account for the complexity and multi-dimensionality of knowledge processes and KC.

The paper provides important practical implications by explicitly accounting for the cultural aspects of the inextricably interrelated nature of the most common knowledge processes in KM initiatives.

KM research has examined a long and varied list of knowledge processes. This has arguably resulted in KM theorizing being fragmented or disintegrated. Whilst it is evident that organisational culture affects persons’ behaviour in the organisation, the impact of persons’ values and beliefs on knowledge processes as a whole remain understudied. This study provides a model of KC. Moreover, the paper offers a novel systematic approach to developing conceptual and theoretical models.

The development of personal relationships between team members is recognised as an important factor in enhancing effective working relationships among members of both co‐located and virtual teams. However, little has been written on how to build these online relationships among virtual team members. This paper reports part of a qualitative research study on how facilitators of virtual teams build and maintain online relationships. In particular, the paper examines how virtual team facilitators use Internet‐based and conventional electronic communication channels to build relationships with their virtual team members. The findings suggest that some electronic communication channels are more effective than others in building online relationships. The paper concludes by suggesting that facilitators need to strategically use the channels available to them to effectively build online relationships.