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Project portfolio management (PPM) is a commonly used technique to align projects with strategy and to ensure adequate resourcing for projects. In this paper, to gain a better understanding of PPM dynamics, a system dynamics (SD) model was developed. To do so, an Iranian independent power producer was used as a case study in the energy sector; moreover, policy options were derived and generalized for such a developer company.

To cope with the complexity of business processes in a power producer company and to formulate an optimum policy, causal relations and loops were derived first and then state-flow diagrams were designed to simulate the problem in the system, as it is usual in the SD methodology.

The proposed model was applied to a real-world case study to rectify managers’ viewpoint about their business dynamics and to formulate new project portfolio strategies to improve the viability of the company. The model proved how a static portfolio analysis can misguide managers in planning their project portfolio strategies, and how effective feedback can improve PPM in developing companies in the energy sector.

Systems approach, especially SD methodology, has been rarely used to analyze PPM problems in the energy sector. This study highlights the implications of feedback and dynamics in PPM and tries to derive optimal value of portfolios.

The Iranian construction industry has been grappling with numerous problems in recent years, including rework, high costs and design errors. Engineers in this field have always highlighted the use of modern technological methods of construction to improve quality and productivity and reduce time and cost. One of these technologies is the so-called building information modeling (BIM), which has been very difficult to adopt and implement in Iran. The purpose of this study is to propose a systemic and holistic model to analyze the dynamics of adoption and implementation of BIM in this country. The purpose of this paper is to understand the dynamics of BIM acceptance to identify the most effective policy to maximize it in the Iranian manufacturing industry.

A two-stage methodology has been developed to achieve the purpose of the research. In the first stage, a technology acceptance model for BIM acceptance was developed using the grounded theory (GT) method. This conceptual model provides a holistic basis for building a simulation model. Thus, in the second stage, we used the dynamics system methodology to extract a dynamic model from the conceptual one. This dynamic model can simulate different policies and may be used to evaluate their respective effectiveness.

In this study, using the GT method, we obtained 510 primary codes, 118 secondary codes, 50 concepts and 17 categories. After determining the relationships between categories through axial coding, we reached a conceptual model based on selective coding. Mention some of the variables of the conceptual model. Awareness, security, perceived usefulness and perceived ease of use are some of the most important variables of this model. In the next part, this conceptual model was run using system dynamics and, thus, turned into a causal model in which all the effective variables on BIM technology and their relationships with each other are specified. The stock and flow diagram of the problem and its related equations were presented. To improve the model and solve the problem, we examined the four policies as four future scenarios on the model: continuing the status quo, development of specialist workforce training, bolstering governmental support and increasing awareness via advertisement within. The simulation results showed that government support is the most effective policy for maximizing BIM acceptance in Iran.

In addition to enumerating all the factors affecting BIM technology, this paper proposes a systemic model that provides an accurate and comprehensive view of the acceptance of this technology. In this regard, by introducing feedback loops, as well as reinforcing and balancing factors versus factors causing stasis, the model offers a much deeper insight into mechanisms associated with BIM development and its barriers. Therefore, this study provides a very useful perspective and basis for policy-makers and all stakeholders to accept and implement BIM technology. The findings of this study can lead to more accurate policy-making, removal of acceptance barriers, promotion of incentives, and consequently more effective acceptance of BIM technology.

In this study, a new mixed research method was used. The innovation of our study lies in its simultaneous use of GT method to construct an accurate and holistic model and applying the system dynamics methodology to build a holistic and systemic model of the BIM acceptance problem. This research also provides a suitable standard and tool for studying BIM technology in developing countries.

Artificial intelligence (AI) is increasingly applied by businesses to optimize their processes and decision-making, develop effective and efficient strategies, and positively influence customer behaviors. Businesses use AI to generate behaviors such as customer retention (CR). The existing literature on “AI and CR” is vastly scattered. The paper aims to review the present research on AI in CR systematically and suggest future research directions to further develop the field.

The Scopus database is used to collect the data for systematic review and bibliometric analysis using the VOSviewer tool. The paper performs the following analysis: (1) year-wise publications and citations, (2) co-authorship analysis of authors, countries, and affiliations, (3) citation analysis of articles and journals, (4) co-occurrence visualization of binding terms, and (5) bibliographic coupling of articles.

Five research themes are identified, namely, (1) AI and customer churn prediction in CR, (2) AI and customer service experience in CR, (3) AI and customer sentiment analysis in CR, (4) AI and customer (big data) analytics in CR, and (5) AI privacy and ethical concerns in CR. Based on the research themes, fifteen future research objectives and a future research framework are suggested.

The paper has important implications for researchers and managers as it reveals vital insights into the latest trends and paths in AI-CR research and practices. It focuses on privacy and ethical issues of AI; hence, it will help the government develop policies for sustainable AI adoption for CR.

To the author's best knowledge, this paper is the first attempt to comprehensively review the existing research on “AI and CR” using bibliometric analysis.

Supplier evaluation and selection is an essential (multi-criteria decision-making) MCDM process that considers qualitative and quantitative factors. This research work attempts to use a MCDM technique based on merging fuzzy Technique for Order Preference by Similarity to Ideal Solution (F-TOPSIS) and Quality Function Deployment (QFD) ideas. The study attempts to find the supplier's attributes (HOWs) to accomplish its goals after determining the product's characteristics to suit the company's needs (WHATs).

The proposed research methodology comprises the following four steps: Step 1: Determine the product purchase requirements (“WHATs”) and those pertinent to supplier evaluation (“HOWs”). In Step 2, the relative importance of the “WHAT-HOW” correlation scores is determined and also the resulting weights of “HOWs”. In Step 3, linguistic evaluations of possible suppliers in comparison to subjective criteria are given to the decision-makers. Step 4 combines the QFD and F-TOPSIS techniques to select suppliers.

A fuzzy MCDM method based on fusing and integrating fuzzy information and QFD is presented to solve the drawbacks of conventional decision-making strategies used in supplier selection. Using the F-TOPSIS method, fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS), the relative closeness coefficient values for all alternatives are computed. The suppliers are ranked by relating the closeness of coefficient values. This method permits the combination of ambiguous and subjective data expressed as fuzzy-defined integers or linguistic variables.

QFD and TOPSIS, two widely used approaches, are combined in this article to rank and evaluate suppliers based on the traits that the suppliers choose to prioritize. This study demonstrates that the method employed could address multiple-criteria decision-making scenarios in a computationally efficient manner. The effectiveness and applicability of the method are illustrated using an example.

This paper aims to study the hub transportation system in supply chain networks which would contribute to reducing costs and environmental pollution, as well as to economic development and social responsibility. As not all customers tend to buy green products, several customer groups should be considered in terms of need type.

In this paper, a multi-objective hub location problem is developed for designing a sustainable supply chain network based on customer segmentation. It deals with the aspects of economic (cost reduction), environment (minimizing greenhouse gas emissions by the transport sector) and social responsibility (creating employment and community development). The epsilon-constraint method and augmented epsilon-constraint (AEC) method are used to solve the small-sized instances of this multi-objective problem. Due to the non-deterministic polynomial-time hardness of this problem, two non-dominated sorting genetic algorithm-II (NSGA-II) and multi-objective grey wolf optimizer (MOGWO) metaheuristic algorithms are also applied to tackle the large-sized instances of this problem.

As expected, the AEC method is able to provide better Pareto solutions according to the goals of the decision-makers. The Taguchi method was used for setting the parameters of the two metaheuristic algorithms. Considering the meaningful difference, the MOGWO algorithm outperforms the NSGA-II algorithm according to the rate of achievement to two objectives simultaneously and the spread of non-dominance solutions indexes. Regarding the other indexes, there was no meaningful difference between the performance of the two algorithms.

The model of this research provides a comprehensive solution for supply chain companies that want to achieve a rational balance between the three aspects of sustainability.

The importance of considering customer diversity on the one hand and saving on hub transportation costs, on the other hand, triggered us to propose a hub location model for designing a sustainable supply chain network based on customer segmentation.