Search results

The purpose of this paper is to explore the relationship between big data and knowledge management (KM).

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.

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.

The focus of the study was on oil and gas sector, and, thus, the research results may lack generalizability.

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 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.

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.

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.

The focus of the study was on the oil and gas sector, and thus the research results may lack generalizability.

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.

The purpose of this paper is to investigate the critical types of knowledge lost when employees depart companies in the oil and gas field.

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.

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.

The study findings might only be applicable to the oil and gas sector.

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.

This paper aims to describe a conceptual design of Personal Learning Environment & Network (PLE&N) and a learning model developed in support of peer-based social and lifelong learning in higher education, which collaborate with classroom learning.

The model consists of students, instructors and external parties interacting synergistically in learning in PLE&N-enabled courses based on the collaborative designs of instructor-led pedagogical approach and external parties-assisted lifelong learning “first-mover” development. The research constructs, tests and assesses this model in courses of 12 subjects in nearly two years.

The practicality of the designs is evidenced in post-course surveys and reflected by students’ ability in productively using collaborative resources over the internet to create an ever-expanding personal learning space stretching from home to campus and beyond, oriented toward individuality, universality, ubiquity, interactivity and connectivity.

The research contributes to PLE&N, social and lifelong learning seamless integration in theory and practice to dramatically enhance students’ virtual learning skills.

This meta-analytic study tries to synthesize the mixed relationships between knowledge management technologies (KMT) and organizational performance as well as aims to explore the impacts of contextual elements, such as national culture, economy and industries, on these relationships.

Findings on various subjects from 40 previous empirical studies were examined using meta-analysis.

It was found that KMT are positively related to overall organizational performance as well as financial and nonfinancial performance and that the relationship between KMT and financial performance is stronger in developing economies than in developed economies.

It helps practitioners better understand the role of KMT in organizational performance in various contexts and provides practical suggestions for KMT implementation.

As the first meta-analytic study to address the generalizability of KMT–organizational performance relationships, this paper offers an improved understanding of the benefits of KMT. It also expands knowledge about how contextual issues related to national culture, economies and industries affect KMT payoffs.

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 paper is to propose an automated machine learning (AutoML) and multi-agent system approach to improve overall product delivery satisfaction under limited resources.

An AutoML method is purposed to model delivery satisfaction of individual customer, and a heuristic method and multi-agent system are proposed to improve overall satisfaction under limited processing capability. A series of simulation experiments have been conducted to illustrate the effectiveness of the proposed methodology.

The simulated results show that the proposed method can effectively improve overall delivery satisfaction, especially when the demand of customer orders is highly fluctuating and when the customer satisfaction models are highly diversified.

The proposed framework provides a more dynamic and continuously improving way to model delivery satisfaction of individual customer, thereby supports companies to provide personalized services and develop scalable and flexible business at a lower cost, and ultimately improves the overall quality, efficiency and effectiveness of delivery services.

The proposed methodology utilizes AutoML and multi-agent system to model individual customer delivery satisfaction and improve the overall satisfaction. It can cooperate with the existing delivery resource planning methods to further improve customer delivery satisfaction. The authors propose an AutoML approach to model individual customer delivery satisfaction, which enables continuous update and improvements. The authors propose multi-agent system and a heuristic method to improve overall delivery satisfaction. The numerical results show that the proposed method can improve overall delivery satisfaction with limited processing capability.

The purpose of this paper is to embark on evaluating the role of Chat Generative-Trained Transformer (ChatGPT) in personal knowledge management (PKM) practices of individual knowledge workers across varied disciplines.

The methodology involves four steps, i.e. literature search, screening and selection of relevant data, data analysis and data synthesis related to KM, PKM and generative artificial intelligence (AI) with a focus on ChatGPT. The findings are then synthesized to develop a viewpoint on the challenges and opportunities brought by ChatGPT for individual knowledge workers in enhancing their PKM capability.

This work highlights the prevailing challenges and opportunities experienced by knowledge workers while leveraging PKM through implying ChatGPT. It also encapsulates how some management theories back the cruciality of generative AI (specifically ChatGPT) for PKM.

This study identifies the challenges and opportunities. from existing studies and does not imply empirical data/result. The authors believe that findings can be adjusted to diverse domains regarding knowledge workers’ PKM endeavors. This paper draws some conclusions and calls for further empirical research.

ChatGPT’s capability to accelerate organizational performance compelled scholars to focus in this domain. The linkage of ChatGPT to Knowledge Management is an under-explored area specifically the role of ChatGPT on PKM hasn't been given attention in the existing work. This is one of the earliest studies to explore this context.

In view of the elderly caregiving service being in high demand nowadays, the purpose of this paper is to develop an intelligent e-healthcare system for the domestic care industry by using the Internet of Things (IoTs) and Fuzzy Association Rule Mining (FARM) approach.

The IoTs connected with the e-healthcare system collect real-time vital sign monitoring data for the e-healthcare system. The FARM approach helps to identify the hidden relationships between the data records in the e-healthcare system to support the elderly care management tasks.

To evaluate the proposed system and approach, a case study was carried out to identify the association between the specific collected demographic data, behavior data and the health measurements data in the e-healthcare system. It is found that the discovered rules are useful for the care management tasks in the elderly healthcare service.

Knowledge discovery in databases uses various data mining techniques and rule-based artificial intelligence algorithms. This paper demonstrates complete processes on how an e-healthcare system connected with IoTs can support the elderly care services via a data collection phase, data analysis phase and data reporting phase by using the FARM to evaluate the fuzzy sets of the data attributes. The caregivers can use the discovered rules for proactive decision support of healthcare services and to improve the overall service quality by enhancing the elderly healthcare service responsiveness.

The purpose of this article is to investigate how organization retain the knowledge of Contract Workforce (CWF) and to understand the associated challenges in this regard.

Adopting an inductive approach, 15 semi-structured interviews were conducted with senior managers, project heads and consultants working in leading oil and gas companies across eight countries (USA, Australia, UAE, KSA, Pakistan, UK, Thailand and Russia). Thematic analysis was carried out to analyze the data collected.

CWF appears to be a significant source of knowledge attrition and even knowledge loss in the oil and gas sector. There are various risks associated with hiring of CWF, such as hallowing of organizational memory, repeated training of contractors, no knowledge base, workforce shortage among others which can impede the knowledge retention capability of O&G companies in the context of contract workforce. Various knowledge retention strategies for CWF have been revealed, however, there is interplay of various factors such as proportion of CWF deployed, proper resource utilization, cross-functional multi-level teams' involvement and strength of transactional ties. Maintaining strong relationships (Transactional ties) is crucial to maintain a virtual organizational memory (partial knowledge retention) and to follow a adopting a rehired when required policy.

The knowledge retention issue in the context of CWF has not be addressed in past researches. This article attempts to fill this gap.