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Article
Publication date: 13 February 2017

Muhammad Saleem Sumbal, Eric Tsui and Eric W.K. See-to

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

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Abstract

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.

Details

Journal of Knowledge Management, vol. 21 no. 1
Type: Research Article
ISSN: 1367-3270

Keywords

Article
Publication date: 10 July 2017

Muhammad Saleem Sumbal, Eric Tsui, Eric See-to and Andrew Barendrecht

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…

2518

Abstract

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.

Details

Journal of Knowledge Management, vol. 21 no. 4
Type: Research Article
ISSN: 1367-3270

Keywords

Article
Publication date: 25 May 2018

Muhammad Saleem Sumbal, Eric Tsui, Ricky Cheong and Eric W.K. See-to

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

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Abstract

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.

Details

Journal of Knowledge Management, vol. 22 no. 7
Type: Research Article
ISSN: 1367-3270

Keywords

Article
Publication date: 8 May 2017

Haley Wing Chi Tsang and Eric Tsui

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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

Details

VINE Journal of Information and Knowledge Management Systems, vol. 47 no. 2
Type: Research Article
ISSN: 2059-5891

Keywords

Article
Publication date: 29 August 2019

Muhammad Saleem Sumbal, Eric Tsui, Irfan Irfan, Muhammad Shujahat, Elaine Mosconi and Murad Ali

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…

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Abstract

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.

Details

Journal of Knowledge Management, vol. 23 no. 8
Type: Research Article
ISSN: 1367-3270

Keywords

Article
Publication date: 29 November 2018

W.M. Wang, J.W. Wang, A.V. Barenji, Zhi Li and Eric Tsui

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Practical implications

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.

Originality/value

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.

Details

Industrial Management & Data Systems, vol. 119 no. 4
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 14 August 2017

Bennie Wong, G.T.S. Ho and Eric Tsui

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…

Abstract

Purpose

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.

Design/methodology/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.

Findings

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.

Originality/value

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.

Details

Industrial Management & Data Systems, vol. 117 no. 7
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 28 December 2021

M. Saleem Ullah Khan Sumbal, Irfan Irfan, Susanne Durst, Umar Farooq Sahibzada, Muhammad Adnan Waseem and Eric Tsui

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.

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 9 October 2020

Ioanna Pavlidou, Savvas Papagiannidis and Eric Tsui

This study is a systematic literature review of crowdsourcing that aims to present the research evidence so far regarding the extent to which it can contribute to…

Abstract

Purpose

This study is a systematic literature review of crowdsourcing that aims to present the research evidence so far regarding the extent to which it can contribute to organisational performance and produce innovations and provide insights on how organisations can operationalise it successfully.

Design/methodology/approach

The systematic literature review revolved around a text mining methodology analysing 106 papers.

Findings

The themes identified are performance, innovation, operational aspects and motivations. The review revealed a few potential directions for future research in each of the themes considered.

Practical implications

This study helps researchers to consider the recent themes on crowdsourcing and identify potential areas for research. At the same time, it provides practitioners with an understanding of the usefulness and process of crowdsourcing and insights on what the critical elements are in order to organise a successful crowdsourcing project.

Originality/value

This study employed quantitative content analysis in order to identify the main research themes with higher reliability and validity. It is also the first review on crowdsourcing that incorporates the relevant literature on crowdfunding as a value-creation tool.

Article
Publication date: 4 April 2016

Chui Ling Yeung, Chi Fai Cheung, Wai Ming Wang, Eric Tsui and Wing Bun Lee

Narratives are useful to educate novices to learn from the past in a safe environment. For some high-risk industries, narratives for lessons learnt are costly and limited…

Abstract

Purpose

Narratives are useful to educate novices to learn from the past in a safe environment. For some high-risk industries, narratives for lessons learnt are costly and limited, as they are constructed from the occurrence of accidents. This paper aims to propose a new approach to facilitate narrative generation from existing narrative sources to support training and learning.

Design/methodology/approach

A computational narrative semi-fiction generation (CNSG) approach is proposed, and a case study was conducted in a statutory body in the construction industry in Hong Kong. Apart from measuring the learning outcomes gained by participants through the new narratives, domain experts were invited to evaluate the performance of the CNSG approach.

Findings

The performance of the CNSG approach is found to be effective in facilitating new narrative generation from existing narrative sources and to generate synthetic semi-fiction narratives to support and educate individuals to learn from past lessons. The new narratives generated by the CNSG approach help students learn and remember important things and learning points from the narratives. Domain experts agree that the validated narratives are useful for training and learning purposes.

Originality/value

This study presents a new narrative generation process for a high-risk industry, e.g. the construction industry. The CNSG approach incorporates the technologies of natural language processing and artificial intelligence to computationally identify narrative gaps in existing narrative sources and proposes narrative fragments to generate new semi-fiction narratives. Encouraging results were gained through the case study.

Details

Journal of Knowledge Management, vol. 20 no. 2
Type: Research Article
ISSN: 1367-3270

Keywords

1 – 10 of 138