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The purpose of this paper is to propose a data-driven scheme for identifying critical project complexity dimensions and establishing the trade-off across multiple project performance criteria.

This paper adopts a hybrid approach using Bayesian Belief Networks (BBNs) and Artificial Neural Networks (ANNs). The output of the ANN model is used as input to the BBN model for prioritizing project complexity dimensions relative to multiple project performance criteria. The proposed process is demonstrated through a real application in the construction industry.

With a number of nonlinear interactions involved within and across project complexity and performance, it is not feasible to model and assess the strength of these interactions using conventional techniques. The proposed process helps in effectively mapping a “multidimensional complexity” space to a “multidimensional performance” space and makes use of data from past projects for operationalizing this mapping scheme by means of ANNs. This obviates the need for developing a parametric model that is both challenging and computationally cumbersome. The mapping function can be used for generating all possible scenarios required for the development of a data-driven BBN model.

This paper introduces a data-driven process for operationalizing the mapping of project complexity to project performance within a network setting of interacting complexity dimensions and performance criteria. The results of the application study manifest the importance of capturing the interdependency across project complexity and performance. Ignoring the underlying interdependencies and relying exclusively on conventional correlation-based techniques may lead to making suboptimal decisions.

The purpose of this paper is to examine the strategic response to Industry 4.0 for Chinese automotive industry and to identify the critical factors for its successful implementation.

A technological, organizational, and environmental framework is used to build the structural models, and statistical tools are used to validate the model. The data analysis helps to determine which factors have impact on the strategic response and whether their relationships are positive or negative. Interpretive structural modeling method is applied to further analyze these derived factors for depicting the relationship.

The result shows that company size and nature do not increase the use of advanced production technologies, while other factors have positive impacts on improving the technology adoption among the companies surveyed.

A strategic response to Industry 4.0 not only helps in improving organizational competitiveness, but it also has social and economic implications. For this purpose, empirical data are collected to measure the understanding of Industry 4.0 in the Chinese automotive industry.

Despite the fact that the Chinese Government has proposed the “Made in China 2025” approach as a way to promote smart manufacturing, little empirical evidence exists in the literature validating company’s perspective toward Industry 4.0. This paper is to fill the research gap.

Reverse logistics services are designed to move goods from their point of consumption to an endpoint to capture value or properly dispose of products and materials. Artificial intelligence (AI)-based reverse logistics will help Micro, Small, and medium Enterprises (MSMEs) adequately recycle and reuse the materials in the firms. This research aims to measure the adoption of AI-based reverse logistics to improve circular economy (CE) performance.

In this study, we proposed ten hypotheses using the theory of natural resource-based view and technology, organizational and environmental framework. Data are collected from 363 Indian MSMEs as they are the backbone of the Indian economy, and there is a need for digital transformation in MSMEs. A structural equation modeling approach is applied to analyze and test the hypothesis.

Nine of the ten proposed hypotheses were accepted, and one was rejected. The results revealed that the relative advantage (RA), trust (TR), top management support (TMS), environmental regulations, industry dynamism (ID), compatibility, technology readiness and government support (GS) positively relate to AI-based reverse logistics adoption. AI-based reverse logistics indicated a positive relationship with CE performance. For mediation analysis, the results revealed that RA, TR, TMS and technological readiness are complementary mediation. Still, GS, ID, organizational flexibility, environmental uncertainty and technical capability have no mediation.

The study contributed to the CE performance and AI-based reverse logistics literature. The study will help managers understand the importance of AI-based reverse logistics for improving the performance of the CE in MSMEs. This study will help firms reduce their carbon footprint and achieve sustainable development goals.

Few studies focused on CE performance, but none measured the adoption of AI-based reverse logistics to enhance MSMEs’ CE performance.

This study aims to establish an evaluation method for cross-regional major infrastructure project (CRMIP) supportability. The focus is to identify evaluation indicators from a complexity perspective and develop an evaluation model using qualitative and quantitative methods. Case studies are carried out to verify the reliability of the evaluation model, thereby providing theoretical and practical guidance for CRMIP operations and maintenance (O&M).

Guided by the idea of complexity management, the evaluation indicators of CRMIP supportability are determined through literature analysis, actual O&M experience and expert interviews. A combination of qualitative and quantitative methods, consisting of sequential relationship analysis, entropy weighting, game theory and cloud model, is developed to determine the indicator weights. Finally, the evaluation model is used to evaluate the supportability of the Hong Kong–Zhuhai–Macao Bridge (HZMB), which tests the rationality of the model and reveals its supportability level.

The results demonstrate that CRMIPs' supportability is influenced by 6 guideline-level and 18 indicator-level indicators, and the priority of the influencing factors includes “organization,” “technology,” “system,” “human resources,” “material system,” and “funding.” As for specific indicators, “organizational objectives,” “organizational structure and synergy mechanism,” and “technical systems and procedures” are critical to CRMIPs' O&M supportability. The results also indicate that the supportability level of the HZMB falls between good and excellent.

Under the guidance of complexity management thinking, this study proposes a supportability evaluation framework based on the combined weights of game theory and the cloud model. This study provides a valuable reference and scientific judgment for the health and safety of CRMIPs' O&M.

This paper aims to propose a comprehensive framework for prioritizing complexity criteria. The framework was validated by applying in infrastructure international development (ID) project as a case study.

A literature review highlighted the limitations of existing complexity prioritization methods. Then, a combination of the fuzzy decision-making trial and evaluation laboratory (DEMATEL) and fuzzy analytic network process (ANP) was employed as a foundation to develop a three-stage complexity prioritization framework. Focus group discussion and questionnaire surveys were used to practically test the framework in the infrastructure ID projects.

The three-stage complexity prioritization framework was validated to be reliable and feasible. The findings showed ability of consultants, scope uncertainties, site compensation and clearance, communication between stakeholders, administrative procedure and project duration were the most significant complexity criteria of ID projects in the Vietnamese context.

The framework is a robust tool that enables the researchers to grasp the interaction of complexity criteria for complexity prioritization. Later studies can apply the proposed framework, with some minor revisions, to assess the interaction of criteria in other research topics in, and beyond, project complexity. Results of the case study suggest project stakeholders focusing on complex interactions among criteria to reduce project complexity.

This study contributes to the body of knowledge by providing a comprehensive complexity prioritization framework that grasps the interrelationship of complexity criteria. For stakeholders of ID projects, the findings provide insightful perspectives to understand complexity, which can help to enhance project performance.

The purpose of this research study was to understand the simultaneous competitive and social gains of machine learning (ML) and artificial intelligence (AI) usage in organizations. There was a knowledge hiatus regarding the contribution of the deployment of ML and AI technologies and their effects on organizations and society.

This study was grounded on the dynamic capabilities (DC) and ML and AI automation-augmentation paradox literature. This research study examined these theoretical perspectives using the response of 239 Indian organizational chief technology officers (CTOs). Partial least square-structural equation modeling (PLS-SEM) path modeling was applied for data analysis.

The results indicated that ML and AI technologies organizational usage positively influenced DC initiatives. The findings depicted that DC fully mediated ML and AI-based technologies' effects on firm performance and social performance.

This study contributed to theoretical discourse regarding the tension between organizational and social outcomes of ML and AI technologies. The study extended the role of DC as a vital strategy in achieving social benefits from ML and AI use. Furthermore, the theoretical tension of the automation-augmentation paradox was explored.

Organizations deploying ML and AI technologies could apply this study's insights to comprehend the organizational routines to pursue simultaneous competitive benefits and social gains. Furthermore, chief technology executives of organizations could devise how ML and AI technologies usage from a DC perspective could help settle the tension of the automation-augmentation paradox.

Increased ML and AI technologies usage in organizations enhanced DC. They could lead to positive social benefits such as new job creation, increased compensation to skilled employees and greater gender participation in employment. These insights could be derived based on this research study.

This study was among the first few empirical investigations to provide theoretical and practical insights regarding the organizational and societal benefits of ML and AI usage in organizations because of their DC. This study was also one of the first empirical investigations that addressed the automation-augmentation paradox at the enterprise level.

The purpose of this paper is to provide a holistic view of the link between the identification of complexity contributing factors, the application of project management approaches and their impacts on the performance of Information Technology (IT)-enabled change projects.

A qualitative approach of an embedded single-case design comprising three IT-enabled change projects delivered in Australia was used to explore the impact of complexity contributing factors and project management approaches on project performance measures. Semi-structured interviews were used as the main data collection method. Thematic analysis was used as the data analysis approach.

The results from the thematic analysis highlight that complexity contributing factors are related to two categories of complexity defined in this paper: technical uncertainties and uncertainty in goals and deliverables, both have an impact on the performance of IT-enabled change projects. It also highlights key project management approaches such as the use of an adaptive management approach and good communication as key to managing complexity. It also identifies a misalignment between stakeholder perception of success and the project management success measure for complex IT-enabled projects.

The research is based on data collected from Australian participants involved in three case studies. Additional data collection and reviews from practitioners in the field of project management could further refine and improve this research.

The research facilitates the identification of specific complexity contributing factors at the early stage of a project to ensure that the appropriate project management approaches and success measures are used.

The paper contributes to rethinking the pathways towards improving project performance in the IT sector by expanding the identification of project complexity to understanding how complexity and the management approaches impact project performance.

Project complexity (PC) governs project success, but the project management literature primarily focuses on performance measures and rarely examines the complexity factors, especially for megaprojects. This paper aims to determine the most significant complexity factors for the railway megaprojects in India.

A mixed approach using the Delphi and best–worst method (BWM) helped to identify, validate and determine the most critical factors that require intervention to diminish variance from project performance.

The BWM resulted in stakeholder management, followed by organizational and technological complexity as significant complexity factors, and the varied interests of the stakeholder as the most important among the 40 subfactors.

The finding indicates the necessity for strategic, tactical and operational-level interventions to effectively manage the complexity affecting project efficiency because of the varied stakeholders. This paper will guide the project and general managers to prioritize their resources to handle complexity for effective project performance measured in terms of time, cost and quality and help them make strategic decisions. The research findings of this study are expected to help researchers and practitioners in better planning and smoother execution of projects. In addition, this study would help the researchers formulate policies and strategies for better handling of the projects.

This study adds significant value to the body of knowledge related to PC in megaprojects in developing countries. The result of the investigation underlined that nine complexity factors and seven unique subfactors, namely, the sustainable environment, timely availability of information, communication in both directions, interdepartmental dependency and coordination, design, statutory norms, site challenges, socioeconomic conditions, the tendency of staff to accept new technology and the frequent changes in the requirements of stakeholders are significant in railway megaprojects. The BWM is applied to rank the complexity factors and subfactors in the case area.

The purpose of this paper is to enhance the understanding of what project complexity is, what drivers and factors that influence complexity and how consequences for organizational performance can be assessed.

The research is explanatory and based on literature review, model development, interviews and case studies. The model is validated through a case study.

The findings are a model for identifying and analyzing complexity drivers and complexity factors. The model starts with generic complexity drivers such as ambiguity, uncertainty, unpredictability and pace. These drivers are in each project influenced by nature and by socio-political, economic and technological surroundings to result in complexity factors that are specific to the project analyzed. The model can be used to analyze project complexity and to define requirements for the organization of the project and guidelines for the execution.

The research is limited to large projects with a technical delivery of some kind of facilities.

The model can be used to assess the required capability of the organization for successful project execution.

The contribution of the research is a new model for understanding project complexity. The distinction between project complexity drivers and factors is essential as well as the taxonomy for the factors building on and adding to already published research.

The complexity of projects has become a serious issue and obstacle in their successful completion. In order to overcome these complexities, it has become imperative to identify the relevant management competencies of project managers. The purpose of this paper is to address the problem of cost, time and scope in engineering infrastructure projects due to their complexities through management competencies.

In the first phase of the study, 32 experts were interviewed through semi-structured pre-tested questionnaire. In this phase, essential elements of complexities were identified initially. This was followed by finding required dimensions of competencies to counter these complexities and to acquire improved performance. In the final stage, required levels of competencies for specific elements of complexity were identified. In the second phase, 85 “project managers” were also approached to get feedback about their recently completed public sector engineering infrastructure projects in Pakistan.

The study identified additional dimensions, i.e. honesty, enthusiasm and dedication, in the case of competencies and adverse law and order situation, political instability, land issues, energy crisis and weak authorization of project managers in the case of complexities. Leadership, management skill, communication skill, effectiveness and result orientation were identified as top quality traits required. The study concluded that there is a significant impact of management competencies and complexities on project performance.

The study contributes to a better understanding of how to improve performance in complex engineering infrastructure projects through adopting management competencies. It also empirically illustrates the relations among project management competencies, complexities and project performance. Although the research is grounded on public sector infrastructure projects, its findings may also be helpful for practices in project management of other sectors.