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Gives an overview of quantitative model‐based research in operations management, focusing on research methodology. Distinguishes between empirical and axiomatic research, and furthermore between descriptive and normative research. Presents guidelines for doing quantitative model‐based research in operations management. In constructing arguments, builds on learnings from operations research and operations management research from the past decades and on research from a selected number of other academic disciplines. Concludes that the methodology of quantitative model‐driven empirical research offers a great opportunity for operations management researchers to further advance theory.

The bid/no-bid decision is critical to the success of construction contractors. The factors affecting the bid/no-bid decision are either qualitative or quantitative. Previous studies on modeling the bidding decision have not extensively focused on distinguishing qualitative and quantitative factors. Thus, the purpose of this paper is to improve the bidding decision in construction projects by developing tools that consider both qualitative and quantitative factors affecting the bidding decision.

This study proposes a mixed qualitative-quantitative approach to deal with both qualitative and quantitative factors. The mixed qualitative-quantitative approach is developed by combining a rule-based expert system and fuzzy-based expert system. The rule-based expert system is used to evaluate the project based on qualitative factors and the fuzzy expert system is used to evaluate the project based on the quantitative factors in order to reach the comprehensive bid/no-bid decision.

Three real bidding projects are used to investigate the applicability and functionality of the proposed mixed approach and are tested with experts of a construction company in Alberta, Canada. The results demonstrate that the mixed approach provides a more reliable, accurate and practical tool that can assist decision-makers involved in the bid/no-bid decision.

This study contributes theoretically to the body of knowledge by (1) proposing a novel approach capable of modeling all types of factors (either qualitative or quantitative) affecting the bidding decision, and (2) providing means to acquire, store and reuse expert knowledge. Practical contribution of this paper is to provide decision-makers with a comprehensive model that mimics the decision-making process and stores experts' knowledge in the form of rules. Therefore, the model reduces the administrative burden on the decision-makers, saves time and effort and reduces bias and human errors during the bidding process.

If the use of information technology (IT) supporting clinical trial projects offers opportunities to optimize the underlying information management process, the intricacy of the identification and evaluation of relevant IT options is generally seen as a complex task in healthcare. Hence, the purpose of this paper is to examine the problem of ex ante information system evaluation, and assess the impact of IT on the information management process underlying clinical trials.

Combining Unified Modeling Language (UML) and system dynamics modeling, a simulation model for evaluating IT was developed. This modeling effort relies on a case study conducted in a clinical research organization, which, at that time, faced an IT investment dilemma.

Some illustrative results of sensitivity analyzes conducted on error rates in clinical data transmission are presented. These simulation results allow for quantifying the impact of different IT options on human resources' efforts, time delays and costs of clinical trials projects. Notably, the results show that although the technology has no real influence on the duration of a clinical trial project, it impacts the number of projects that can be carried out simultaneously.

The research provides insights into the development of an innovative approach appropriate to the evaluation of IT supporting clinical trials, through the use of a mixed‐method based on qualitative and quantitative modeling. The results illustrate two critical issues addressed in the IS literature: the necessity to extend IT evaluation beyond the quantitative‐qualitative dichotomy; and the role of evaluation in organizational learning, and in learning about business dimensions.

Contemporary literature reveals that, to date, the poultry livestock sector has not received sufficient research attention. This particular industry suffers from unstructured supply chain practices, lack of awareness of the implications of the sustainability concept and failure to recycle poultry wastes. The current research thus attempts to develop an integrated supply chain model in the context of poultry industry in Bangladesh. The study considers both sustainability and supply chain issues in order to incorporate them in the poultry supply chain. By placing the forward and reverse supply chains in a single framework, existing problems can be resolved to gain economic, social and environmental benefits, which will be more sustainable than the present practices.

The theoretical underpinning of this research is ‘sustainability’ and the ‘supply chain processes’ in order to examine possible improvements in the poultry production process along with waste management. The research adopts the positivist paradigm and ‘design science’ methods with the support of system dynamics (SD) and the case study methods. Initially, a mental model is developed followed by the causal loop diagram based on in-depth interviews, focus group discussions and observation techniques. The causal model helps to understand the linkages between the associated variables for each issue. Finally, the causal loop diagram is transformed into a stock and flow (quantitative) model, which is a prerequisite for SD-based simulation modelling. A decision support system (DSS) is then developed to analyse the complex decision-making process along the supply chains.

The findings reveal that integration of the supply chain can bring economic, social and environmental sustainability along with a structured production process. It is also observed that the poultry industry can apply the model outcomes in the real-life practices with minor adjustments. This present research has both theoretical and practical implications. The proposed model’s unique characteristics in mitigating the existing problems are supported by the sustainability and supply chain theories. As for practical implications, the poultry industry in Bangladesh can follow the proposed supply chain structure (as par the research model) and test various policies via simulation prior to its application. Positive outcomes of the simulation study may provide enough confidence to implement the desired changes within the industry and their supply chain networks.

The purpose of this study is to formulate exploratory scenarios of Operations Research through the critical uncertainty approach and Soft Systems Methodology.

In this study, to formulate plausible scenarios, the discipline of operation research internal and external experts’ opinions of this field have been gathered through Delphi approach and uncertainty questionnaires. After use of the most important uncertainties, plausible scenarios of operations research have been mapped with the help of experts through co-thinking workshops.

Four scenarios are presented in this study. These scenarios include Solar System, Esfandiar's Eye, Rival’s Setraps and Legendary Simurgh. Naturally, the imagination of such a unitary future for all academic communities is an expectation far from reality, and given the conditions of each of these futures or any integration of them is imaginable.

Operations Research models have been faced with variously multiple changes since its emergence until now. Investigation into the future of operations research on the necessity for his planning has not received a reasonable notice in the literature. Sporadic activities that have been carried out are also lacking in the necessary methodology. Also, there has been no research about future study using the soft Operation Research tools (Soft Systems Methodology).

Knowing which of the hundreds of elements that comprise a consumer's purchasing decision are the most important is essential if leaders are to wisely allocate resources and support actions that will have an expedient impact on growth.

IBM Consulting is testing consumer decision process (CDP) modeling in a variety of industries.

A new tool, CDP modeling, offers companies a combination of traditional market research and unique quantitative modeling can take the guesswork out of why consumers do or do not buy.

Comparative testing with other consumer decision research tools needs to be done.

Achieving the benefits of CDP requires starting with strategic issues like competitive gaps, selecting consumer decisions that provide the best information for this issue, like why consumers choose a particular retailer, and implementing changes based on insights discovered.

Armed with insights based on CDP modeling that better explain why consumers choose certain products, channels and competitors over others, companies can market existing products more effectively than their rivals and take market share from them.

According to the “strategic focus and future orientation” principle of the integrated reporting (<IR>) framework, <IR> should provide information useful to support investors in assessing the future financial performance of organizations. This study aims to support the operationalization of this function by improving the forward-looking orientation of the integrated report.

Basing on the backward- and forward-looking disclosure in <IR> and the dynamic resource-based view (DRBV), this study develops an explorative case study building a quantitative simulation model based on an integrated report.

This study provides useful insights into how operationalizing the <IR> “future orientation” and obtaining more quantitative information on the organization’s capacity to create value in the future by applying DRBV and quantitative simulation modeling.

The article presents one case study to explore the method suggested to improve the <IR> forward-looking orientation. Additional case studies applying the same research design should be certainly useful to refine the method.

Supporting the <IR> forward-looking orientation, this study provides additional information for the decision-making process of investors, thus contributing to the efficient and productive allocation of capital.

Few studies have investigated forward-looking information in integrated reports, highlighting the existence of an “information gap” referred to such disclosure. Overcoming these previous results, the study provides useful insights on how to improve the <IR> forward-looking orientation.

It has been widely recognized that agility enables manufacturing systems to respond to dynamic and unpredictable changes in today’s competitive environment. Develops a quantitative analysis framework and a simulation methodology to explore the value of agility in financial terms. Addresses the issues pertaining to the assessment of how an agile system performs in an environment of unanticipated changes, the comparison between two or more systems with different designs and hence different agility levels and the justification of investments in agility. Proposes an exploratory framework for a structured analysis of the various segments of the manufacturing system in which agility at different levels is built‐in through different pathways and links it to a set of aggregate performance measures. Then develops a simulation model that captures dynamic and unanticipated changes in the operating environment and facilitates performance appraisal and investment justification decisions using a quantitative financial metric.

The purpose of this study includes two parts. First, it introduces a machine-based method for model and instrument development and updating that integrates large sample qualitative data. Second, a new model and instrument for e-commerce customer satisfaction are developed.

The research occurs in two phases. In Phase 1, data collection occurs with a literature-based quantitative model and instrument that includes at least one qualitative scale item per construct. Data analysis of the resulting data includes factor analysis (FA) and latent semantic analysis text mining to generate an updated model and instrument. In Phase 2, data collection uses the new model and instrument. Data analysis in Phase 2 includes exploratory data analysis with FA, exploratory structural equation modeling and partial least square modeling.

As a result of the information gained by the integration of qualitative scales in the literature-based survey, the final model departs substantially from the initial research-based research model. It integrates many of the constructs known to impact a website and software usability from information systems research into a new e-retail satisfaction model.

The research method, as presented here, offers a strategy for integrating large scale qualitative data for refinement of models and the development of instruments. It is essentially a method of gaining the wisdom of crowds economically while simultaneously reducing the biases and laborious effort commonly associated with qualitative research.

The risk management of transitory risk for real assets has gained large interest especially in the past 10 years among researchers as well as market participants. In addition, the recent regulatory tightening in the EU urges financial market participants to disclose sustainability-related financial risk, without providing any methodological guidance. The purpose of the study is the identification and explanation of the methodological limitations in the field of transitory risk modeling and the logic step to advance toward a stochastic approach.

The study reviews the literature on deterministic risk modeling of transitory risk exposure for real estate highlighting the heavy methodological limitations. Based on this, the necessity to model transitory risk stochastically is described. In order to illustrate the stochastic risk modeling of transitory risk, the empirical study uses a Markov Switching Generalized Autoregressive Conditional Heteroskedasticity model to quantify the carbon price risk exposure of real assets.

The authors find academic as well as regulatory urgency to model sustainability risk stochastically from a conceptual point of view. The own empirical results show the superior goodness of fit of the multiregime Markov Switching Generalized Autoregressive Conditional Heteroskedasticity in comparison to their single regime peer. Lastly, carbon price risk simulations show the increasing exposure across time.

The practical implication is the motivation of the stochastic modeling of sustainability-related risk factors for real assets to improve the quality of applied risk management for institutional investment managers.

The present study extends the existing literature on sustainability risk for real estate essentially by connecting the transitory risk management of real estate and stochastic risk modeling.