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.

The literature on the knowledge management relatively ignores an important concept, the individual knowledge management engagement-the degree to which a knowledge worker is involved with the knowledge management-related activities. This concept is imperative for nurturing the productivity of knowledge workers, knowledge management architecture effectiveness, and innovation. Therefore, this study proposes the mediating role of knowledge-worker productivity between individual knowledge management engagement and innovation. The data

Purpose The purpose of this paper is to investigate the critical types of knowledge lost when employees depart companies in the oil and gas field. Design/methodology/approach The study adopts a grounded theory methodology. Twelve semi-structured interviews were conducted with elite informants in the oil and gas sector to gain an in-depth insight into the research problem. ATLAS.ti was used for data analysis and coding. Findings In the oil and gas industry, employees generally have job rotation and work at various geographical locations during their career. The departing employees possess valuable types of knowledge depending on the role and duties they have performed over the years. These include specialized technical knowledge, contextual knowledge of working at different geographical locations, knowledge of train wrecks and history of company, knowledge of relationships and networks, knowledge of business processes and knowledge of management. Research limitations/implications The study findings might only be applicable to the oil and gas sector. Originality/value This paper fulfills an identified gap on the identification of critical areas of knowledge loss when employees depart from oil and gas companies. The study adds to the existing body of literature on this underexplored area in the knowledge management literature.

Purpose The purpose of this paper is to investigate how companies are handling the issue of knowledge retention from old age retiring workers in the oil and gas sector. This is achieved by providing a detailed insight on the challenges and strategies related to knowledge retention through study of companies from different geographical locations across the globe. Design/methodology/approach The study adopts a qualitative research methodology and 20 semi-structured interviews, with open-ended and probing questions, were conducted to gain an in-depth insight into the knowledge retention phenomena. Findings Knowledge retention activities tend to be inconsistent in majority of the oil and gas companies, with not much work being done regarding knowledge loss from old employees, partly because of the fall in oil prices and layoffs. Oil prices turn out to be a decisive factor in oil and gas industry regarding workforce and knowledge retention activities. The political situation and geographical locations of the companies also affect the knowledge retention activities. Moreover, the aging workforce and retirement issue is more acute in the upstream sector. Research limitations/implications The focus of the study was on the oil and gas sector, and thus the research results may lack generalizability. Originality/value This paper fulfills an identified need for investigating the issues and challenges of knowledge retention regarding old age retiring employees by taking into account a global perspective and providing a comparison among different companies in different geographical locations.

Purpose The purpose of this study is to develop a conceptual framework on knowledge loss in a manufacturing sector based on three aspects: likelihood of knowledge loss, critical areas of knowledge loss and relevance of each of these knowledge areas in terms of utilization and alignment with organizational goals and strategy. Such a conceptual framework can be helpful to the practicing managers in understanding the types of knowledge that is lost of a given departing employee and thus deciding on a measure to retain the critical employees or capture their knowledge before they leave. Design/methodology/approach Using a case study approach, data has been collected from a multinational battery manufacturing company based in Hong Kong. Semi-structured interviews have been conducted and analyzed through CAQDAS ATLAS.ti to generate the themes which were then used to develop the conceptual framework. Findings The findings revealed that the likelihood factors of knowledge loss in the manufacturing sector include layoffs, retirement, immigration and job change. The critical areas of knowledge loss comprise the knowledge of relationships and networks, especially with the customers and suppliers, the technical knowledge (battery and process technology) and knowledge of management, among others. The relevance of each of these knowledge areas needs to be determined through proper analysis whether these knowledge areas are needed in future projects, up to date and aligned with organizational goals and strategy along with other factors. Research limitations/implications Using the developed conceptual framework, managers and executives can identify critical employees in the manufacturing sector and accordingly take some appropriate measures to retain their knowledge. Caution should be taken while applying the findings of this study in other industries and context. Originality/value This paper is an attempt to reduce the dearth of empirical studies by exploring knowledge retention in the manufacturing sector, especially in the development of proper conceptual frameworks to assess the potential knowledge loss of employees.

Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Originality/value 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.

PurposeRecent research has highlighted the beneficial role of supply chain resilience for ensuring efficient production and business processes. The purpose of this study is to explore enablers of supply chain resilience. In particular, the authors examine whether and how dynamic capabilities and knowledge management can help firms develop a resilient supply chain in times of high disruption and uncertainty.Design/methodology/approachA single longitudinal case study design was adopted. Data was collected over 8 years from a Pakistani textile producer and supplier through semi-structured interviews and was analyzed through NVivo to generate codes and themes that contributed to the development of the supply chain resilience model.FindingsThe analysis of case study shows that our focal firm strategically acquired, transferred and integrated market knowledge by investing in digital technologies and idiosyncratic resources and consequently developed a supply chain model that was resilient in addressing logistics and delivery challenges in uncertain & critical times.Research limitations/implicationsThe study brings together three main research streams of organizational theory, namely supply chain, knowledge management and dynamic capabilities, and proposes a nuanced resilient supply chain model.Practical implicationsBy applying the research findings, managers can adjust, develop and adopt supply chain resilience to address market volatilities, thereby creating value and longevity in their supply chain operations. However, the findings are context specific and should be applied cautiously.Originality/valueThe outcomes provide early hints on how companies in emerging economies can adopt and integrate novel digital technologies, and overhaul their organizational routines to facilitate knowledge management and develop dynamic capabilities, and consequently enhance the resilience of their supply chain operations.

Purpose Drawing upon the theoretical underpinning of knowledge worker productivity, this study aims to examine the relationship between abusive supervision and knowledge management (KM) process (creation, application and sharing of knowledge) and its impact on the knowledge worker productivity in knowledge-intensive organizations. Design/methodology/approach Hypothesis were tested through PROCESS Macro in IBM SPSS v.26 on a sample of 204 employees working in banking sector of Pakistan. Confirmatory factor analysis was conducted to test the model fitness through AMOS v. 26. Findings The results showed that the relationship between abusive supervision and KM process (creation, application and sharing of knowledge) is negative and highly significant, i.e. greater the abusive supervision in the banking sector, the lower is the engagement in KM processes. Furthermore, there is a positive and highly significant relationship between the KM process and knowledge worker productivity. Finally, the study indicates the negative impact of abusive supervision on the knowledge worker productivity through the mediating mechanism of knowledge management processes. Research limitations/implications A key limitation is that the study is cross-sectional, and the findings may only be generalizable to developing countries context. Originality/value Previous studies have focused on supervisor–employee relationship but not in the context of knowledge worker productivity. This article fulfills this gap through understanding the impact of abusive supervision on the knowledge worker productivity in relation to KM processes (knowledge creation, sharing and application) by drawing upon the theoretical underpinning of knowledge worker productivity.