Proposing a maturity model for the management of human resource information

Abstract: The study explores how the use of human resource information in organisations can be optimised. An extensive literature review was conducted to explore how human resource information is used and managed in organisations. The study is qualitative in nature and employed semi-structured interviews. Interviews with 12 human resource practitioners and human capital consultants were analysed to understand the challenges they experience in managing human resource information. Findings of the study reveal that while organisations expect their human resource information systems to deliver high-level information regarding their people, the approach of organisations to human resource information and the systems used to manage such information may be unstructured. It was also observed that organisations cannot use human resource information at certain complex levels without mastering fundamental level(s). Based on the findings, a four-stage maturity model for the management of human resource information is proposed. The maturity model, when implemented, has the potential to provide organisations with a structured approach to managing and using human resource information, in a manner which will contribute to the optimal use of human resource information systems

Big Data Management in Education Sector: an Overview

The advancement in technological innovation has given rise to a new trend known as Big Data today. Given the soaring popularity of big data technology, organisations are profoundly attracted to and interested in it to transform their organisation by improving their businesses. Big data is enabling organisations to outpace their competitors and save cost. Similarly, the application of Big Data management in Universities is an essential aspect to institutions that have Big Data to manage; as the use of Big Data in the higher education sector is increasing day by day. Many studies have been carried out on big data and analytics with little interest in its management. Big Data management is a reality that represents a set of challenges involving Big Data modeling, storage, and retrieval, analysis, and visualization for several areas in organizations. This paper introduces and contributes to the conceptual and theoretical understanding of Big Data management within higher education as it outlines its relevance to higher education institutions. It describes the opportunities this growing research area brings to higher education as well as major challenges associated with it

Electronic information sharing in local government authorities: Factors influencing the decision-making process

This is the post-print version of the final paper published in International Journal of Information Management. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Local Government Authorities (LGAs) are mainly characterised as information-intensive organisations. To satisfy their information requirements, effective information sharing within and among LGAs is necessary. Nevertheless, the dilemma of Inter-Organisational Information Sharing (IOIS) has been regarded as an inevitable issue for the public sector. Despite a decade of active research and practice, the field lacks a comprehensive framework to examine the factors influencing Electronic Information Sharing (EIS) among LGAs. The research presented in this paper contributes towards resolving this problem by developing a conceptual framework of factors influencing EIS in Government-to-Government (G2G) collaboration. By presenting this model, we attempt to clarify that EIS in LGAs is affected by a combination of environmental, organisational, business process, and technological factors and that it should not be scrutinised merely from a technical perspective. To validate the conceptual rationale, multiple case study based research strategy was selected. From an analysis of the empirical data from two case organisations, this paper exemplifies the importance (i.e. prioritisation) of these factors in influencing EIS by utilising the Analytical Hierarchy Process (AHP) technique. The intent herein is to offer LGA decision-makers with a systematic decision-making process in realising the importance (i.e. from most important to least important) of EIS influential factors. This systematic process will also assist LGA decision-makers in better interpreting EIS and its underlying problems. The research reported herein should be of interest to both academics and practitioners who are involved in IOIS, in general, and collaborative e-Government, in particular

Big Data Management in Education Sector: an Overview

The advancement in technological innovation has given rise to a new trend known as Big Data today. Given the soaring popularity of big data technology, organisations are profoundly attracted to and interested in it to transform their organisation by improving their businesses. Big data is enabling organisations to outpace their competitors and save cost. Similarly, the application of Big Data management in Universities is an essential aspect to institutions that have Big Data to manage; as the use of Big Data in the higher education sector is increasing day by day. Many studies have been carried out on big data and analytics with little interest in its management. Big Data management is a reality that represents a set of challenges involving Big Data modeling, storage, and retrieval, analysis, and visualization for several areas in organizations. This paper introduces and contributes to the conceptual and theoretical understanding of Big Data management within higher education as it outlines its relevance to higher education institutions. It describes the opportunities this growing research area brings to higher education as well as major challenges associated with it

The role of tacit knowledge in the construction industry: towards a definition

The construction industry is perceived as one of the knowledge-based value creating sectors of the economy; however, it faces many challenges, especially in terms of performance, due to its intrinsic nature. Different knowledge-based solutions have been proposed in the past to overcome this problem. However, the process-based solutions, enhancing personalisation strategies and interactions between construction workers to generate and share tacit knowledge, would be much more relevant to overcome KM problems in construction organisations. As the initial step towards the management of tacit knowledge, this paper examines the nature and importance of tacit knowledge in the construction industry. Based on research findings a definition for tacit knowledge is synthesised to: understanding, capabilities, skills and the experiences of individuals; often expressed in human actions in the form of thoughts, points of view, evaluation and advice; generated and acquired through past experiences, individuals, and repositories; utilised for the benefit of individual and organisational development

Barriers to build asset adaptation in private service sector

It is becoming increasingly acknowledged that adaptation and mitigation are equally important and often interrelated approaches to climate change. Recent adaptation initiatives in the UK include the promotion of many policies, reporting and economic support in the public sector. However, adaptation in the private sector still lacks such structured initiative and is initiated largely in response to external forces. This paper presents a review of UK-based adaptation initiatives and presents a study of the adaptation decisionmaking process for the built assets of a large private sector organisation. The study was undertaken as a part of a PhD research programme that evaluated the usefulness of the UKCIP Risk, Uncertainty and Decision-making Framework as well as the UKCIP 02 climate change projections for facilities management decision-making. The decision-making framework and projections were used by a group of facilities personnel responsible for built asset management to explore various climate risks and develop adaptation solutions. The paper reports on issues associated with implementing the first three stages of the decision-making framework, in particular the problems faced by facilities management professionals in operationalising the risks and evaluating solutions. The following findings were drawn. A) Adaptation in the private sector is initiated against an external change or signal, for example market forces or experience of a climate-related extreme event. B) For many built asset professionals the transformation of scientific climate change data into impacts on their built asset is a demanding task in terms of required knowledge and time. This process is further complicated by the long time horizon (30 years) associated with climate projections compared to the short time horizon (5-10 years) for strategic business decisions, and the uncertainty attached to climate change projections. C) As a result of (B), much of the analysis for decisionmaking remains qualitative and semi-quantitative and lacks gravitas when hard financial decisions have to be made. D) The perception and attitude of managerial and strategic decision-making personnel towards climate change shapes the decision-making process and adaptation option selection. E) Adaptive capacity, in terms of the time, finance and expertise available to organisations is important to achieving successful adaptation goals. Although, the new UKCP09 projections have been made available since the completion of the study, many of the findings are generic in nature and directly applicable to these new tools. In conclusion, by conceptualising the observed adaptation process with that of organisation learning, it is suggested that literature on organisation learning is likely to provide an effective basis for understanding and promoting the adaptation in the private sector

Big data and smart cities: a public sector organizational learning perspective

Public sector organizations (city authorities) have begun to explore ways to exploit big data to provide smarter solutions for cities. The way organizations learn to use new forms of technology has been widely researched. However, many public sector organisations have found themselves in new territory in trying to deploy and integrate this new form of technology (big data) to another fast moving and relatively new concept (smart city). This paper is a cross-sectional scoping study—from two UK smart city initiatives—on the learning processes experienced by elite (top management) stakeholders in the advent and adoption of these two novel concepts. The findings are an experiential narrative account on learning to exploit big data to address issues by developing solutions through smart city initiatives. The findings revealed a set of moves in relation to the exploration and exploitation of big data through smart city initiatives: (a) knowledge finding; (b) knowledge reframing; (c) inter-organization collaborations and (d) ex-post evaluations. Even though this is a time-sensitive scoping study it gives an account on a current state-of-play on the use of big data in public sector organizations for creating smarter cities. This study has implications for practitioners in the smart city domain and contributes to academia by operationalizing and adapting Crossan et al’s (Acad Manag Rev 24(3): 522–537, 1999) 4I model on organizational learning

Applying semantic web technologies to knowledge sharing in aerospace engineering

This paper details an integrated methodology to optimise Knowledge reuse and sharing, illustrated with a use case in the aeronautics domain. It uses Ontologies as a central modelling strategy for the Capture of Knowledge from legacy docu-ments via automated means, or directly in systems interfacing with Knowledge workers, via user-defined, web-based forms. The domain ontologies used for Knowledge Capture also guide the retrieval of the Knowledge extracted from the data using a Semantic Search System that provides support for multiple modalities during search. This approach has been applied and evaluated successfully within the aerospace domain, and is currently being extended for use in other domains on an increasingly large scale