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Methodological pluralism in consumer research is usually confined to post‐positivist interpretive approaches. Argues, however, that a positivistic stance, radical behaviourism, can enrich epistemological debate among researchers with the recognition of radical behaviourism′s ultimate reliance on interpretation as well as science. Although radical behaviourist explanation was initially founded on Machian positivism, its account of complex social behaviours such as purchase and consumption is necessarily interpretive, inviting comparison with the hermeneutical approaches currently emerging in consumer research. Radical behaviourist interpretation attributes meaning to behaviour by identifying its environmental determinants, especially the learning history of the individual in relation to the consequences similar prior behaviour has effected. The nature of such interpretation is demonstrated for purchase and consumption responses by means of a critique of radical behaviourism as applied to complex human activity. In the process, develops and applies a framework for radical behaviourist interpretation of purchase and consumption to four operant equifinality classes of consumer behaviour: accomplishment, pleasure, accumulation and maintenance. Some epistemological implications of this framework, the behavioural perspective model (BPM) of purchase and consumption, are discussed in the context of the relativity and incommensurability of research paradigms. Finally, evaluates the interpretive approach, particularly in terms of its relevance to the nature and understanding of managerial marketing.

The purpose of this paper is to propose a stochastic optimisation model for integrating service and maintenance policies in order to solve the queuing problem and the cost of maintenance activities for public transport services, with a particular focus on urban ropeway system.

The authors adopt the following approaches: a discrete-event model that uses a set of interrelated queues for the formulation of the service problem using a cost-based expression; and a maintenance model consisting of preventive and corrective maintenance actions, which considers two different maintenance policies (periodic block-type and age-based).

The work shows that neither periodic block-type maintenance nor an age-based maintenance is necessarily the best maintenance strategy over a long system lifecycle; the optimal strategy must consider both policies.

The maintenance policies are then evaluated for their impact on the service and operation of the transport system. The authors conclude by applying the proposed optimisation model using an example concerning ropeway systems.

This is the first study to simultaneously consider maintenance policy and operational policy in an urban aerial ropeway system, taking up the problem of queuing with particular attention to the unique requirements public transport services.

This paper aims to develop a dynamic civil facility degradation prediction model to forecast the reliability performance tendency and remaining useful life under imperfect maintenance based on the inspection records and the maintenance actions.

A real-time hidden Markov chain (HMM) model is proposed in this paper to predict the reliability performance tendency and remaining useful life under imperfect maintenance based on rare failure events. The model assumes a Poisson arrival pattern for facility failure events occurrence. HMM is further adopted to establish the transmission probabilities among stages. Finally, the simulation inference is conducted using Particle filter (PF) to estimate the most probable model parameters. Water seals at the spillway hydraulic gate in a Taiwan's reservoir are used to examine the appropriateness of the approach.

The results of defect probabilities tendency from the real-time HMM model are highly consistent with the real defect trend pattern of civil facilities. The proposed facility degradation prediction model can provide the maintenance division with early warning of potential failure to establish a proper proactive maintenance plan, even under the condition of rare defects.

This model is a new method of civil facility degradation prediction under imperfect maintenance, even with rare failure events. It overcomes several limitations of classical failure pattern prediction approaches and can reliably simulate the occurrence of rare defects under imperfect maintenance and the effect of inspection reliability caused by human error. Based on the degradation trend pattern prediction, effective maintenance management plans can be practically implemented to minimize the frequency of the occurrence and the consequence of civil facility failures.

Purpose – The purpose of this paper is to help build up a relevant database for mapping technical and financial effectiveness of production in order to make cost‐effective maintenance decisions. Design/methodology/approach – A theoretical model is developed based on past research and experience adopting a holistic systems approach on the production. A case study, which includes databases of two maintenance‐used software programs, verifies the potential of applying the model. Findings – The main result achieved is a model for identifying relevant data required for accurate problem tracing and localisation within maintenance and production processes using a top down approach. The main conclusions are integration of IT and data resources within the enterprise is needed for developing a holistic view of the production process and a well‐formulated and documented procedure of data identification will ensure that the data can be traced back to root sources and in this way we can support the work of continuous cost‐effective improvement by eliminating root causes of problems at an early stage. Research limitations/implications – Further model verification by industrial case studies would be of interest. Practical implications – The holistic approach and the model presented are applicable especially in capital intensive industries, where maintenance budget is not negligible and the amount of data to process is large. By structuring the data need and data identification process relevant performance measures will be monitored and advanced maintenance concepts can be applied. Originality/value – By applying the proposed model in industry, the data identification process itself and not the data contents is necessary to be standardised and structured. It shifts the focus of the quality aspect from just data level to both data and data collection level. The performance measures will therefore not be chosen depending on what the IT applications can provide in first hand, but upon what is needed for cost‐effective mapping, analysis, following up and assessment of maintenance performance.

The paper aims to identify the critical constraints that impede heritage building (HB) facility managers from discharging their duties effectively and develop an index model to guide HB maintenance management (HBMM) practitioners to the critical constraints.

A literature review was conducted to identify HBMM constraints. Facilty management practitioners assessed the constraints' significance through an online survey. The factor analysis was used to shortlist and group the constraints, and the constraint clusters were analyzed by the fuzzy synthetic evaluation technique. A significant index cluster to determine HBMM constraints criticality was generated using the linear additive model.

Embracing a total of 16 HBMM constraints, the three clusters identified are: (1) managerial and inadequacy constraints, (2) pressure and bureaucracy constraints and (3) HB peculiarities constraints. Based on the generated significant index, the HB peculiarities cluster was identified as the most significant.

The study was conducted in a particular jurisdiction, limiting the generalizability of the result. Future research should address this limitation by covering more jurisdictions.

The significant index model (SIM) developed enables HBMM practitioners to objectively assess the criticality of HB constraints and facilitates them to effectively strategize and allocate resources for HBMM.

The SIM, which transforms subjective judgment into the objective assessment of the HBMM constraints' criticality, can assist practitioners, policymakers and other HBMM stakeholders in implementing strategies for the sustainability of HBs.

This chapter gives in “Introduction to the Human Capital Issue” a critical analysis of the standard (economic) Human Capital (HC) theory, with the help of some “traditional” (founding) accounting concepts. From this study, to avoid the accounting and social issues highlighted in “Introduction to the Human Capital Issue,” we present, in “The “Triple Depreciation Line” Model and the Human Capital,” the “Triple Depreciation Line” (TDL) accounting model, developed by Rambaud & Richard (2015b), and we apply it to “HC,” but viewed as genuine accounting capital – a matter of concern – that firms have to protect and maintain.

From a critical review of literature on HC theory, from the origin of this concept to its connection with sustainable development, this chapter provides a conceptual discussion on this notion and on the differences/common points between capital and assets in accounting and economics. Then, it uses a normative accounting model (TDL), initially introduced to extend, in a consistent way, financial accounting to extra-financial issues.

This analysis shows at first that the standard (economic) HC theory is based on a (deliberate) confusion between assets and capital, in line with a standard economic perspective on capital. Therefore, this particular viewpoint implies: an accounting issue for reporting HC, because “traditional” accounting capital and assets are clearly isolated concepts; and a societal issue, because this confusion leads to the idea that HC does not mean that human beings are “capital” (i.e., essential), or have to be maintained, even protected, for themselves. It only means that human beings are mere productive means. The application of the TDL model to an accounting redefinition of HC allows a discussion about some key issues involved in the notion of HC, including the difference between the standard and “accounting” narratives on HC. Finally, this chapter presents some important consequences of this accounting model for HC: the disappearance of the concept of wage and the possibility of reporting repeated (or continuous) use of HC directly in the balance sheet.

This chapter contributes to the literature on HC and in general on capital and assets, by stressing in particular some confusions and misunderstandings in these concepts. It fosters a cross-disciplinary approach of these issues, through economic, accounting, and sustainability viewpoints. This analysis also participates in the development of the TDL model and the research project associated. It finally proposes another perspective, more sustainable, on HC and HC reporting.

The stakes of HC are important in today’s economics, accounting, and sustainable development. The different conceptualizations of HC, and the narratives behind it, may have deep social and corporate implications. In this context, this analysis provides a conceptual, and practicable, framework to develop a more sustainable concept of HC and to enhance working conditions, internal business relations, integrated reporting. As an outcome of these ideas, this chapter also questions the standard corporate governance models.

This chapter gives an original perspective on HC, and in general on the concept of capital, combining an economic and an accounting analysis. It also develops a new way to report HC, using an innovative integrated accounting model, the TDL model.

Factory management needs to find the technically feasible point up to which maintenance and quality activity levels of a factory should be selected to achieve highest productivity with a view to fulfil company objectives for higher profitability. This research paper aims to report the development of an analytical relation between maintenance, quality and productivity.

An analytical relation is designed and developed incorporating graphical analysis and regression analysis to support outputs of graphical method, which creates an appropriate model. A mixed research approach is used, including application of a practical case example.

The study displays a wide range of productivity performance profiles with the strategic aim of identifying the technically feasible highest productivity result obtained. It has formulated an important analytical link, and suggested a few recommendations. The formulated model predicts the best possible productivity result out of maintenance and quality‐related practical data of a factory. The analysis enables managers to analyze, compare and identify improvement opportunities in order to enjoy competitive advantages.

Modern factory managers, particularly in food production, who live in a world of rapid changes, extensive interactions and complex situations, and face everyday challenges in a competitive global market, can use this model as a hands‐on tool for measurement, evaluation, logically better and proper realistic planning and implementation of maintenance and quality activities to attain maximum manufacturing productivity for their companies.

The model is the result of an attempt to design and develop, fulfil identified technical managerial needs and offer practical help to make logical decisions. Evidence from the literature confirms that this is a newer outlook on analytical diagnostic tools, which demonstrates the weaknesses in existing factory production system and thus helps in identifying key areas for productivity improvements. Therefore, this research is one step to further the potential and practical value added contribution in food and other industries.

The purpose of this paper is to develop a novel model for maintenance backlog (MB) of physical assets and structure it in a framework for integrated planning (IPL).

Reliability theory principles for modelling MB are used. Furthermore, to structure a framework for IPL, literature study combined with earlier case studies is used.

The framework for IPL facilitates the model of MB. In addition to providing real-time diagnosis indicators, MB is regarded as valuable information for decision support in IPL.

Development of MB applied to IPL.

The purpose of this paper is to review cotemporary maintenance programs and analyze factory production data for an SF₆ gas filled circuit breaker population. Various maintenance techniques and studies are reviewed to understand the reliability of circuit breaker models and the impact manufacturing can have on long term maintenance considerations.

Production and field event data were analyzed using statistical analysis tools. The population data were formatted so that a recurrent event analysis could be conducted to establish the mean cumulative function (MCF) by model and product family (class). Average Field Two‐year Recorded Event Rate (AFTRER) is introduced and compared to commonly used Field Incident Rate (FIR) and Mean‐Time between Failure (MTBF) measures.

Common managerial operating questions can be answered as exhibited for the provided circuit breaker population. This includes the longevity of field issues, the anticipated life cycle of a model or class, and AFTRER for models or classes of interest. These statistical analysis tools are used to make critical production quality and asset management observations and aid in decision‐making.

Due to limitations in existing database systems, the cost of events and explanatory variables related to event rates were not included in the analyses. There remains much work to be done in terms of the installation and retro‐fitting of breakers with conditions monitors in the field.

A framework to analyze maintenance data from fleet of similar assets using recurrent event data analysis is provided. The methods illustrated here would be useful for quality and asset managers to make operating decisions. This includes resource allocation decisions across a network of equipment.

Data analyzed are for power circuit breakers which are a critical element in the operation and reliability of the US power grid.

Using recurrent event data analysis to review and develop solutions to production quality and asset management problems including a comparison of AFTRER to FIR and MTBF measures.

This paper proposes the extension of a prognosis process by means of the integration of maintenance alternative impacts in order to develop a maintenance decision‐making tool.

The deployment of this extended prognosis process follows a methodology based both on probabilistic and on event approaches.

The importance of the maintenance function has increased due to its role in keeping and improving the system availability and safety but also the product quality. To support this new role, the maintenance concept has undergone several major developments to lead to proactive considerations mainly based on prognosis process allowing one to select the best maintenance plan to be carried out.

Studies over the last 20 years have indicated that around Europe the direct cost of maintenance is equivalent to between 4 and 8 per cent of total sales turnover. The indirect cost of maintenance is likely to be a similar amount. Thus, in the countries where modern maintenance practices have yet to be well adopted by industry, the potential savings from modern maintenance are massive. These modern and efficient maintenances imply identifying the root‐cause of component failures, reducing the failures of production systems, eliminating costly unscheduled shutdown maintenances, and improving productivity as well as quality. It means, for the companies, migrating from their traditional reactive approach, which is “fail and fix”, to “predict and prevent”. The advantage of the latter is that maintenance is performed only when a certain level of equipment deterioration occurs. This “proactive” maintenance is mainly based on prognosis process often considered as the Achilles heel, while its goal is fundamental for implementing anticipation capabilities. This paper looks into this issue by proposing the development of an innovative prognosis process integrating the modelling of maintenance actions and their impacts on system performances. It leads to offering a maintenance aided decision‐making tool cable of assisting the decision‐maker in selecting the best maintenance plan to be carried out.

The feasibility of this new prognosis is experimented on the manufacturing Tele‐Maintenance (TELMA) platform supporting the unwinding of metal bobbins.