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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 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.

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.

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.

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.

This is the first typology of data-based decision-making considering advanced analytics.

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.

Business analytics (BA) has been a breakthrough technological development in recent years. Although scholars have suggested several solutions in using these technologies to facilitate decision-making, there are as of yet limited studies on how analysts, in practice, improve decision makers' understanding of business environments. This study uses sensemaking theory and proposes a model of how data analysts generate analytical outcomes to improve decision makers' understanding of the business environment.

This study employs an interpretive field study with thematic analysis. The authors conducted 32 interviews with data analysts and consultants in Australia and New Zealand. The authors then applied thematic analysis to the collected data.

The thematic analysis discovered four main sensegiving activities, including data integration, trustworthiness analysis, appropriateness analysis and alternative selection. The proposed model demonstrates how these activities support the properties of sensemaking and result in improved decision-making.

This study provides strong empirical evidence for the theory development and practice of sensemaking. It brings together two distinct fields – sensemaking and business analytics – and demonstrates how the approaches advocated by these two fields could improve analytics applications. The findings also propose theoretical implications for information system development (ISD).

This study demonstrates how data analysts could use analytical tools and social mechanisms to improve decision makers' understanding of the business environment.

This study is the first known empirical study to conceptualize the theory of sensemaking in the context of BA and propose a model for analytical sensegiving in organizations.

This paper aims to investigate the tacit knowledge-sharing framework among Pakistani academicians. The objective is to study trust and social networks as antecedents to foster tacit knowledge sharing with the mediating role of commitment. Furthermore, the moderating role of organizational knowledge-sharing culture is also examined.

The study applied a survey-based quantitative research design to test the proposed model. The nature of data are cross-sectional and collected with stratified random sampling among public sector higher education professionals of Pakistan. The total sample size for the present research is 247 respondents. The variance-based structural equation modeling technique by using Smart_PLS software is used for analysis.

Data analysis and results reveal that trust and social networks are significant predictors of tacit knowledge sharing among Pakistani academicians while commitment positively mediated the relationships. While the moderating role of organizational knowledge-sharing culture is also established.

The current research explains tacit knowledge sharing among academics with fewer antecedents i.e. social network and trust with limited sample size and specific population. There is still a great deal of work to be done in this area. Hence, the study provides direction for including knowledge-oriented leadership and knowledge governance in the current framework. Moreover, the framework can be tested in different work settings for better generalization.

The study gives an important lead to practitioners for enhancing tacit knowledge sharing at the workplace through a robust social network of employees, building trust and boosting employees’ commitment, as well as through supportive organizational knowledge sharing culture.

The research comprehends the tacit knowledge sharing framework with theoretical arrangements of trust, social networks, commitment and culture in higher education workplace settings under the umbrella of social capital theory.

The purpose of this study is to examine the role of knowledge management practices, organisational commitment and capacity building on employee performance in the hotel industry. This study also investigated the mediating role of organisational commitment and capacity building between knowledge management practices and employee performance.

A quantitative approach and questionnaire survey were used to collect data from hotel employees from Malaysia. Self-administered questionnaires were distributed to collect data from 291 participants, and partial least squares structural equation modelling was used to analyse the hypotheses.

The results of this study confirm that knowledge management practices positively and significantly affect knowledge-employee performance. Employees achieve this performance through the mediating influence of organisational commitment and capacity building culture.

This study offers several implications for Malaysian practitioners and policymakers regarding learning and knowledge management practices in the hospitality industry. The results suggest that organisations can manage knowledge assets and key processes of the organisational environment to create and use knowledge to improve sustainable employee performance through knowledge management practices.

This study sheds light on the knowledge management literature by examining the effect of knowledge management practices on organisational commitment, particularly in the hospitality industry in Malaysia.

The purpose of this study is twofold: to investigate the role of big data in firms’ co-knowledge and value creation and to understand the underlying drivers behind value creation through big data in the oil and gas industry by underscoring the role of firms’ capabilities, trends and challenges.

Following an inductive approach, semi-structured interviews were conducted with senior managers and analysts working in oil and gas companies across eight countries. The data collected from these key informants were then analysed using the qualitative data analysis software ATLAS.ti.

Value creation through big data is an important factor for enhancing performance. It has a positive impact on both tangible (organisational performance) and intangible (societal) aspects depending on the context. Oil and gas companies understand the importance of big data to creating value in their operations. However, implementing and using big data has been problematic. In this study, a framework was developed to show that factors such as the shortage of data experts, poor data quality, the risk of cyber-attacks and unsupportive organisational cultures impede its implementation and utilisation.

The findings from this study have implications for managers and executives implementing big data and creating value across various data-intensive industries. The research findings, are contextual, however, and should be applied cautiously.

This study contributes to the value creation literature in the big data context. The findings identify the key areas to be considered for the effective implementation and utilisation of big data in the oil and gas sector. This study addresses a broad but under-explored issue (i.e. knowledge creation from big data and its implementation) and strengthens the academic debate within this research stream.

The purpose of this study is to investigate the relationship between team culture and team performance through the mediating role of knowledge sharing and team emotional intelligence.

The study advocated that team culture influences the knowledge sharing behavior of team members and the development of emotional intelligence skill at the team level. Further, it is hypothesized that knowledge sharing and team emotional intelligence positively influence team performance. By adopting a quantitative research design, data were gathered by using a survey questionnaire from 535 respondents representing 95 teams working in private health-care institutions.

The findings significantly indicated that knowledge sharing and team emotional intelligence influence team working. Furthermore, this study confirms the strong association between team culture and team performance through the lens of knowledge sharing and team emotional intelligence.

This investigation offers observational proof to health-care services to familiarize workers with the ability of emotional intelligence and urge them to share knowledge for enhanced team performance. The study provides in-depth understanding to managers and leaders in health-care institutions to decentralize culture at the team level for endorsement of knowledge sharing behavior.

This is amongst one of the initial studies investigating team members making a pool of knowledge to realize potential gains enormously and influenced by the emotional intelligence. Team culture set a platform to share knowledge which is considered one of the principal execution conduct essential for accomplishing and managing team adequacy in a sensitive health-care environment.

This study aims to extend and explore patterns and trends of research in the linkage of big data and knowledge management (KM) by identifying growth in terms of numbers of papers and current and emerging themes and to propose areas of future research.

The study was conducted by systematically extracting, analysing and synthesizing the literature related to linkage between big data and KM published in top-tier journals in Web of Science (WOS) and Scopus databases by exploiting bibliometric techniques along with theory, context, characteristics, methodology (TCCM) analysis.

The study unfolds four major themes of linkage between big data and KM research, namely (1) conceptual understanding of big data as an enabler for KM, (2) big data–based models and frameworks for KM, (3) big data as a predictor variable in KM context and (4) big data applications and capabilities. It also highlights TCCM of big data and KM research through which it integrates a few previously reported themes and suggests some new themes.

This study extends advances in the previous reviews by adding a new time line, identifying new themes and helping in the understanding of complex and emerging field of linkage between big data and KM. The study outlines a holistic view of the research area and suggests future directions for flourishing in this research area.

This study highlights the role of big data in KM context resulting in enhancement of organizational performance and efficiency. A summary of existing literature and future avenues in this direction will help, guide and motivate managers to think beyond traditional data and incorporate big data into organizational knowledge infrastructure in order to get competitive advantage.

To the best of authors’ knowledge, the present study is the first study to go deeper into understanding of big data and KM research using bibliometric and TCCM analysis and thus adds a new theoretical perspective to existing literature.