Using SSM to structure the identification of inputs and outputs in DEA

This paper describes the Soft Information Systems and Technologies Methodology (SISTeM), and demonstrates its utility in integrating informational and organisational development. The methodology and its stages are illustrated with examples from recent information systems and technology (IS&T) based projects, mainly in the health sector. Its antecedents in soft systems methodology are identified. The use of the approaches underpinning concept of the human/machine activity system is described. The nature of SISTeM as a social, political and technological form of contingency methodology, working with other approaches and disciplines, is demonstrated. SISTeM, it will be argued has capacity for orchestrating the emergence, consolidation and continual development of real world actor networks.

This paper explores the issue of whether computer-based support tools can enhance the use of 'soft' systems methodologies as applied to real-world problem situations. Although work has been carried out by a number of researchers in applying computer-based technology to concepts and methodologies relating to 'soft' systems thinking such as Soft Systems Methodology (SSM), such attempts appear to be still in their infancy and have not been applied widely to real-world problem situations. This paper will highlight some of the problems that may be encountered in attempting to develop computer-based support tools for 'soft' systems methodologies. Particular attention will be paid to an attempt by the author to develop a computer-based support tool for a particular 'soft' systems method of inquiry known as the Appreciative Inquiry Method that is based upon Vickers' notion of 'appreciation' (Vickers, 196S) and Checkland's SSM (Checkland, 1981). The final part of the paper will explore some of the lessons learnt from developing and applying the computer-based support tool to a real world problem situation, as well as considering the feasibility of developing computer-based support tools for 'soft' systems methodologies. This paper will put forward the point that a mixture of manual and computer-based tools should be employed to allow a methodology to be used in an unconstrained manner, but the benefits provided by computer-based technology should be utilised in supporting and enhancing the more mundane and structured tasks.

Purpose – The purpose of this paper is to develop a model based on data envelopment analysis (DEA) and program evaluation and review technique/critical path method (PERT/CPM) for determining prospective benchmarks. Design/methodology/approach – The idea of determining prospective benchmark is needed for developing a model for future planning where inputs and outputs of systems are influenced by external factors such as economic conditions, demographic changes, and other socio-economic factors. In this paper, the PERT/CPM method estimates prospective inputs and outputs. On the other hand, in particular systems some measures play the role of both input and output. Such factors in DEA literature are called dual-role factors. This paper integrates PERT/CPM technique and the DEA. Findings – The results of the proposed model depict that a present benchmark may not be a benchmark in future. A numerical example validates the proposed model. Originality/value – This paper, for the first time, applies the PERT/CPM technique to incorporate the ideas for identifying prospective benchmarks. Moreover, the proposed model is an alternative solution for classifying inputs and outputs in DEA. Also, the proposed model is utilized in benchmarking green supply chain management.

Recent papers have provided a number of overviews in connection with the fast growing literature on bad outputs in Data Envelopment Analysis (DEA), such as defective products, emissions and waste. However, there does not seem to exist any comprehensive overview of the opposite phenomenon, particularly not regarding DEA, namely of bads as undesirable objects or factors which are desirable input (flow) into transformation processes, e.g. into waste incineration. Moreover, the terms ‘bad input’ and ‘(un-)desirable input/factor’ are not clearly defined. We use a purely preference-based notion for the desirability of inputs and outputs. A systematic literature search reveals only 22 DEA articles which explicitly address the (desirable) input of bads as original undesirable factors, i.e. as input into the first stage of a single- or multi-stage process. Their detailed analysis shows that current approaches are based on two core ideas involving various efficiency measures. Only four papers deal with real applications of original undesirable factors, namely waste water treatment. Moreover, those disposability assumptions for DEA models often critically discussed in relation to bad outputs (e.g. weak disposability) are not used in these papers, presumably because the processes modelled are themselves disposal processes. Finally, we exemplarily demonstrate how DEA models with bads as inputs (and outputs) can be systematically derived from a decision-theoretic generalization of DEA methodology proposed in the literature.

This paper analyzes the implications of using aggregated value data for both inputs and outputs in Data Envelopment Analysis (DEA). It shows that when total cost and total revenue data is used, the estimated efficiency score reflects profitability efficiency which is composed of technical efficiency, allocative efficiencies on both the input and the output sides, and potential price effects if prices differ between decision-making units. The findings provide practical guidance for using value data in DEA and how to interpret efficiency scores in cases where disaggregated data and price information are not available. • Guidance on interpreting efficiency scores using aggregated value data in Data Envelopment Analysis (DEA). • DEA with total cost and total revenue data estimates profitability efficiency. • Unobserved price differences in value data can raise or lower efficiency estimates.

Academic research is often regarded as less than helpful for practice, while knowledge practitioners are accused of failing to apply relevant research in their industries. This study successfully bridged the theory‑practice gap in that it resolved a real‑world problem and added to the body of knowledge, i.e. it aided to: identify underlying social root causes of a flawed project governance process and associated decision support software; define actions for improvement; and realise generalisable theoretical and applied knowledge to improve both theory and practice. It illustrates that collaborative academic research adds value to both academia and industry. This paper discusses an empirical study where soft systems methodology (SSM) was applied as an interventionist action research (AR) method, positioned in the critical systems thinking (CST) and critical social research (CSR) paradigm. SSM is defined as a learning system that facilitates the identification of actions for improvement of problematical social contexts—it gives epistemological guidance to systematically explore perspectives of individuals involved in and affected by social (e.g. organisational) settings, to identify actions for improvement. However, SSM is found to be most often applied interpretively by academia, i.e., to understand a problem context, rather than to improve it, and by practitioners as a consulting tool. SSM successfully facilitates exploring of problematical social contexts, but, according to users of it, fail to effect real and lasting change. It is also quite difficult to extract generalisable learning from its output. In this study, the researcher argues that SSM have the potential to live up to its original intent. So, she positioned SSM in the CST/CSR paradigm by applying an enriched version of it. She guided participants to reflect on inherent flaws in the scrutinised project governance business process and associated decision support software. Hence, by applying methodological pluralism, she also guided them to reflect on actions for change in light of the organisation’s socially constructed structures and their effects on involved and affected stakeholders of the process. SSM was enriched by also reflecting on the output of facilitated workshops, which aimed to identify required transformation to improve upon deficient project governance, from the perspectives of: the role of the participant(s) in the organisation; the effects that identified organisational decisions and changes may have on them; and by asking probing questions about the crux of statements made, e.g. if it is presumed to be true, what would the effect be, or vice versa. The outcome was emancipation, as key underlying social and structural causes for the organisational deficiencies surfaced.

An integrated fuzzy regression-data envelopment analysis algorithm for optimum oil consumption estimation with ambiguous data

Output–input ratio analysis and DEA frontier

This chapter explores the potential of Soft Systems Methodology (SSM) for making improvements in healthcare practices. SSM is an approach designed for tackling real world problems and is described as a learning process that engages relevant stakeholders in a process of inquiry into a problematic situation with the aim of improving the situation. SSM has potential to contribute to the way we manage change in healthcare by acknowledging the complexities of the real world rather than trying to provide simplified or idealized solutions that all-too-often do not work in reality. Although SSM has not been extensively used in healthcare it has been applied in a variety of healthcare settings and for different types of problematic situations and improvements, indicating the versatility of the methodology. We provide examples of how SSM has been applied and based on our analysis discuss some main principles for how SSM can bring about more responsive, sustainable change in healthcare, including: a participatory approach that involves relevant stakeholders with expertise about the local context; a systems approach to assess and address the problematic situation; and approaching improvements in an iterative learning cycle.

Variable selection in Data Envelopment Analysis

“It depends on the client”—Kees van der Heijden and client‐centric scenario planning: A commentary on Rowland and Spaniol 2021

The objective of this study is to explore the adoption of an intellectual innovation within an organisational environment. The intellectual innovation that will be explored is Soft Systems Methodology.As a prelude to the study, an overview of Soft Systems Methodology is provided. Theoretically, Soft Systems Methodology can be used to formulate a wide range of problem types. However, there is a continual belief that Soft Systems Methodology can only be used to solve soft problems.Innovation Diffusion Theory has been used in a wide range of studies to explain the adoption of innovations within organisations and work groups. This study will evaluate innovation diffusion theory as an effective theory that explains how intellectual innovations can be adopted within organisations.The results indicate that Soft Systems Methodology is an innovation, and that it is useful in formulating problems. Innovation Diffusion theory was evaluated as being an appropriate theory to explain the adoption of an intellectual innovation. The level of adoption and the level of usage by the respondents do not influence how SSM is perceived as being an innovation.There has been little research conducted in the adoption of intellectual innovations. The research presents opportunities for further research within the areas of Innovation Diffusion Theory and Soft Systems Methodology.

Demonstrates the application of Soft Systems Methodology (SSM) to issues facing those information professionals attempting the long-term management of digital data objects (DDOs) in the UK. Reports results of research, undertaken at Loughborough University, Department of Information Science, to demonstrate the benefits of using SSM to determine the critical issues underlying the resolution of complex ill-defined people-centred problems. Defines DDOs as computerized, machine-readable resources, such as Web pages or electronic periodicals, the information content of which can be stored and accessed independently of the form in which it was created. Discusses basic SSM principles and the seven stages of its implementation in research. Secondary source material from a literature review was combined with empirical data and analyzed using SSM. The use of SSM to create a ‘rich picture’ enables the categorization of organizations into relatively homogeneous groups, based on their approach to the long-term management of DDOs. This rich picture illustrates that there is a division between stakeholders who use standard metadata formats and those who have, or are in the process of creating, in house formats.

Soft systems methodology (SSM) is well established as a vehicle for action research, particularly in programmes initiated at Lancaster University. It is now sufficiently well known, in academic circles at least, to be considered as a candidate methodology for a wide range of social science research projects. This paper summarizes (and updates) the set of issues which should be explicit when designing a project which includes both SSM and other research approaches. It attempts to "place' SSM within a taxonomy of social science research approaches, examines the contingencies which may determine the success and acceptance of the research, and explores the relationship between SSM components of such programmes and other, more traditional, approaches. A further important focus is the status of SSM products as theory, and the use of SSM for theory generation and/or testing. © 1997 John Wiley & Sons, Ltd.

This study employs four data envelopment analysis (DEA) models to evaluate the performance efficiency of 21 OECD countries and assess whether the undesirable outputs are over-produced relative to desirable outputs. In evaluating the performance of OECD countries via super-efficiency models, this study focuses on two aspects. First, employing the concept of the Sharpe ratio, we propose another method to deal with undesirable outputs (the unemployment rate, inflation, and air pollution) in DEA. This approach can reveal the relative importance of desirable outputs and undesirable outputs, detect whether undesirable outputs are over-produced, and obtain more accurate efficiency scores. Second, we examine whether knowledge capital can improve a country’s efficiency scores. Our empirical results support the above arguments. In addition, research and development (R&D) expenditures, the proxy variable for knowledge capital, can indeed improve countries’ efficiency scores, implying that the endogenous growth theory is supported in OECD countries. Evidently, whether the undesirable outputs are included in the DEA models and are properly treated is crucial in the evaluation of efficiency values.