Management and engineering of process aware information systems : introduction to the special issue

In this editorial letter, we provide the readers of Information Systems with a birds-eye introduction to Process-aware Information Systems (PAIS) – a sub-field of Information Systems that has drawn growing attention in the past two decades, both as an engineering and as a management discipline. Against this backdrop, we briefly discuss how the papers included in this special issue contribute to extending the body of knowledge in this field.

Strategic Management Practices in the Construction Industry : a Dynamic Capabilities View

This book is an empirical study of strategic management practices in the construction industry. It examines the dynamic capabilities paradigm within the context of the Indonesian construction industry. The characteristics of asset-capability combinations were found to be significant determinants of the competitive advantage of the Indonesian construction enterprises, and that such advantage sequentially contributes to organizational performance. In doing so, this study fills an important gap in the empirical literature and reinforces the dynamic capabilities framework’s recognition as a rigorous theory of strategic management. As the dynamic capabilities framework can work in the context of Indonesia, it suggests that the framework has potential applicability in other emerging and developing countries

Asset health prediction using the explicit hazard model

The ability to estimate the asset reliability and the probability of failure is critical to reducing maintenance costs, operation downtime, and safety hazards. Predicting the survival time and the probability of failure in future time is an indispensable requirement in prognostics and asset health management. In traditional reliability models, the lifetime of an asset is estimated using failure event data, alone; however, statistically sufficient failure event data are often difficult to attain in real-life situations due to poor data management, effective preventive maintenance, and the small population of identical assets in use. Condition indicators and operating environment indicators are two types of covariate data that are normally obtained in addition to failure event and suspended data. These data contain significant information about the state and health of an asset. Condition indicators reflect the level of degradation of assets while operating environment indicators accelerate or decelerate the lifetime of assets. When these data are available, an alternative approach to the traditional reliability analysis is the modelling of condition indicators and operating environment indicators and their failure-generating mechanisms using a covariate-based hazard model. The literature review indicates that a number of covariate-based hazard models have been developed. All of these existing covariate-based hazard models were developed based on the principle theory of the Proportional Hazard Model (PHM). However, most of these models have not attracted much attention in the field of machinery prognostics. Moreover, due to the prominence of PHM, attempts at developing alternative models, to some extent, have been stifled, although a number of alternative models to PHM have been suggested. The existing covariate-based hazard models neglect to fully utilise three types of asset health information (including failure event data (i.e. observed and/or suspended), condition data, and operating environment data) into a model to have more effective hazard and reliability predictions. In addition, current research shows that condition indicators and operating environment indicators have different characteristics and they are non-homogeneous covariate data. Condition indicators act as response variables (or dependent variables) whereas operating environment indicators act as explanatory variables (or independent variables). However, these non-homogenous covariate data were modelled in the same way for hazard prediction in the existing covariate-based hazard models. The related and yet more imperative question is how both of these indicators should be effectively modelled and integrated into the covariate-based hazard model. This work presents a new approach for addressing the aforementioned challenges. The new covariate-based hazard model, which termed as Explicit Hazard Model (EHM), explicitly and effectively incorporates all three available asset health information into the modelling of hazard and reliability predictions and also drives the relationship between actual asset health and condition measurements as well as operating environment measurements. The theoretical development of the model and its parameter estimation method are demonstrated in this work. EHM assumes that the baseline hazard is a function of the both time and condition indicators. Condition indicators provide information about the health condition of an asset; therefore they update and reform the baseline hazard of EHM according to the health state of asset at given time t. Some examples of condition indicators are the vibration of rotating machinery, the level of metal particles in engine oil analysis, and wear in a component, to name but a few. Operating environment indicators in this model are failure accelerators and/or decelerators that are included in the covariate function of EHM and may increase or decrease the value of the hazard from the baseline hazard. These indicators caused by the environment in which an asset operates, and that have not been explicitly identified by the condition indicators (e.g. Loads, environmental stresses, and other dynamically changing environment factors). While the effects of operating environment indicators could be nought in EHM; condition indicators could emerge because these indicators are observed and measured as long as an asset is operational and survived. EHM has several advantages over the existing covariate-based hazard models. One is this model utilises three different sources of asset health data (i.e. population characteristics, condition indicators, and operating environment indicators) to effectively predict hazard and reliability. Another is that EHM explicitly investigates the relationship between condition and operating environment indicators associated with the hazard of an asset. Furthermore, the proportionality assumption, which most of the covariate-based hazard models suffer from it, does not exist in EHM. According to the sample size of failure/suspension times, EHM is extended into two forms: semi-parametric and non-parametric. The semi-parametric EHM assumes a specified lifetime distribution (i.e. Weibull distribution) in the form of the baseline hazard. However, for more industry applications, due to sparse failure event data of assets, the analysis of such data often involves complex distributional shapes about which little is known. Therefore, to avoid the restrictive assumption of the semi-parametric EHM about assuming a specified lifetime distribution for failure event histories, the non-parametric EHM, which is a distribution free model, has been developed. The development of EHM into two forms is another merit of the model. A case study was conducted using laboratory experiment data to validate the practicality of the both semi-parametric and non-parametric EHMs. The performance of the newly-developed models is appraised using the comparison amongst the estimated results of these models and the other existing covariate-based hazard models. The comparison results demonstrated that both the semi-parametric and non-parametric EHMs outperform the existing covariate-based hazard models. Future research directions regarding to the new parameter estimation method in the case of time-dependent effects of covariates and missing data, application of EHM in both repairable and non-repairable systems using field data, and a decision support model in which linked to the estimated reliability results, are also identified.

Empirical study of interactions between knowledge management activities

Purpose – Recent knowledge management (KM) literature suggests that KM activities are not independent of each other, rather they interact with each other to form a process which receives input from both external and internal business environments, and then produces new knowledge for future utilisation. The purpose of this paper is to empirically investigate the relationships between KM activities within the construction business context in order to identify and map the pattern of their interactions. Design/methodology/approach – A questionnaire survey was administered to a sample of contracting organisations operating in Hong Kong to elicit opinions of construction professionals on the intensity of KM activities currently being executed by their organisations in order to facilitate knowledge capture, sharing and utilisation. More than 150 respondents from 99 organisations responded to the survey. Additionally, a total of 15 semi-structured interviews were undertaken to provide a unique perspective on many of the challenges facing local construction organisations when dealing with KM activities. Findings – Knowledge acquisition and utilisation play paramount roles in the development of the organisational knowledge asset. The higher the intensity of these two activities, the larger the organisational knowledge pool which, in turn, demands greater knowledge dissemination capacity. This dissemination capacity enables more active and intense responses to market changes and clients' needs, thus facilitating and stimulating acquisition and utilisation of new tacit knowledge, thus improving organisational business performance. Originality/value – Interactions between KM activities were empirically investigated, from a strategic perspective, in the construction business context.

Waste and resource management practices, legislation and policy encouraging and influencing ‘regeneration reuse’ of property assets

Carbon taxation governance is becoming increasingly popular, further evolving the polluter pays concept already well established in the built environment as a mechanism to controlling and licensing waste generation. This paper presents an explanation of property asset ‘regeneration reuse’ principles following deconstruction, which reduce waste generation associated with the process of demolition, construction and operation. An analysis is made of strategies in Australia and the United Kingdom, comparing jurisdiction targets pertaining to construction and demolition waste that encourage ‘regeneration reuse’. From examination of applicable Australian and United Kingdom legislation, strategic, fiscal and policy that influence on the 'regeneration reuse' of property assets, an evaluation to the variety of issues relevant to waste and resource management practices is reached. The paper concludes that a systematic evaluation framework to selecting building components and structures suitable for reuse after deconstruction must be considered in legislation.

Service management and engineering in information systems research

Service research in information systems (IS) has received attention over many years (e.g. Kettinger and Lee, 1994), but more recently has increased substantially in both diversity and volume (Rai and Sambamurthy, 2006). A service-oriented view of information technology (IT) is gradually taking hold in both academia and industry. This is concomitant with the growth of service-related phenomena and concepts (Lusch and Vargo, 2006), stimulating a global discourse about 'service science' as a new, cross-disciplinary field of research (Chesbrough and Spohrer, 2006).

A business engineering approach to client relationship management in a knowledge based firm

This thesis examines Customer Relationship Management and how the capabilities of an organisation to innovate can be enhanced via its implementation in a Knowledge Based Firm. The research identifies current customer knowledge components within an organisation and identifies for future use, CRM components for implementation within a Knowledge Based Firm. Opinions from a panel of experts' are identified for best practice customer relationship strategy, the most important CRM processes and identification of customer knowledge components that will form the basis of implementing a successful CRM to gain a competitive advantage through enhancing the innovative capability for a Knowledge Based Firm.

Critical success factors for the implementation of a knowledge management system in a knowledge-based engineering firm

This research identifies factors that are crucial to the success of a knowledge management system (KMS) implementation in a prominent Australian engineering consultancy firm. The study employs the Delphi method to solicit the opinions of experienced market leaders in the Australian construction industry, and then benchmarks the organisational profile of the consultancy firm against the Delphi findings. From this comparative case study, recommendations are made pertaining to the organisational and cultural changes required within the consultancy firm in order to improve its readiness to successfully implement a KMS.

Data as an asset: what the oil and gas sector can learn from other industries about “Big Data”

The upstream oil and gas industry has been contending with massive data sets and monolithic files for many years, but “Big Data” is a relatively new concept that has the potential to significantly re-shape the industry. Despite the impressive amount of value that is being realized by Big Data technologies in other parts of the marketplace, however, much of the data collected within the oil and gas sector tends to be discarded, ignored, or analyzed in a very cursory way. This viewpoint examines existing data management practices in the upstream oil and gas industry, and compares them to practices and philosophies that have emerged in organizations that are leading the way in Big Data. The comparison shows that, in companies that are widely considered to be leaders in Big Data analytics, data is regarded as a valuable asset—but this is usually not true within the oil and gas industry insofar as data is frequently regarded there as descriptive information about a physical asset rather than something that is valuable in and of itself. The paper then discusses how the industry could potentially extract more value from data, and concludes with a series of policy-related questions to this end.

Asset revaluations and earnings management: Evidence from Australian companies

This paper examines the association between asset revaluations and discretionary accruals (a proxy for earnings management) using a sample of the largest 300 Australian companies. The results from this study indicate that the revaluation of non-current assets is positively associated with discretionary accruals. This finding is consistent with the argument that revaluation of assets reflects higher agency problems in the form of increased earnings management. Additional findings are that discretionary accruals are higher for firms reporting their non-current assets at fair values appraised by directors, than those of firms that use external appraisers. As well, the choice of auditors and the strength of corporate governance can constrain the opportunistic behaviour of managers in the accounting choice to revalue non-current assets.