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Cause-related B2B marketing programs involve sponsoring organisations working with B2B suppliers with the help of non-profit organisations (NPOs) on practises relating to environmental friendliness, workforce diversity, human rights, safety, philanthropy and business ethics. The study aims to identify the combinatory factors driving the adoption of Digital B2B platforms for managing cause-related B2B marketing programs.

The study adopts an innovative approach of fuzzy-set qualitative comparative analysis (fsQCA) on data collated from top corporations in India supporting cause-related B2B marketing programs. Sponsoring organisations and NPO dyads (i.e. survey both) filled out an email survey on 264 cause-related B2B marketing programs.

The study establishes that the combination of technological, organisational and environmental factors would lead to the adoption of Digital B2B platforms in managing cause-related B2B marketing programs. The study identifies six combinations of these factors for adopting Digital B2B platforms within and across sponsoring organisations and NPOs.

The study findings would aid cause-related B2B marketers in developing Digital B2B platforms’ capabilities by understanding the different combinations of factors driving adoption. Digital B2B platforms’ capabilities can improve market performance if developed as core competencies.

The study findings would enable improvements in the implementation and performance of cause-related B2B marketing programs. Better management of cause-related B2B marketing programs would help increase beneficiary coverage and the realisation of societal goals.

To the author’s knowledge, this is the first study to apply the TOE framework in conjunction with complexity theory to explain the diffusion of adoption of Digital B2B platforms for managing cause-related B2B marketing programs.

Megaproject's construction is essential for the development and economic growth of any country, especially in the developing world. In Kuwait, megaprojects are facing many restrictions that discourage their execution causing a significant delay in bidding, design, construction and operation phases with the execution quality being affected. The objective of this study is to develop a complexity measurement model using analytic hierarchy process (AHP) for megaprojects in Kuwait, with a focus on the New Kuwait University multi-billion campus Shadadiyah (College of Social Science, Sharia and Law (CSSL)) as a case study.

The study applies a hybrid fuzzy analytic hierarchy process (FAHP) method to compare the results with those obtained using the conventional AHP method. This can facilitate the project management activities during the different stages of construction. Data were collected based on the results of a two-round Delphi questionnaire completed by seniors and experts of the selected project.

It was found that project modeling methodology was responsible for complexity. It was grouped under several categories that include technological, goal, organizational, environmental and cultural complexities. The study compares complexity degrees assessed by AHP and FAHP methods. “Technological Complexity” scores highest in both methods, with FAHP reaching 7.46. “Goal Complexity” follows closely behind, with FAHP. “Cultural Complexity” ranks third, differing between methods, while “Organizational” and “Environmental Complexity” consistently score lower, with FAHP values slightly higher. These results show varying complexity levels across dimensions. Assessing and understanding such complexities were essential toward the completion of such megaprojects.

The contribution of this study is on providing the empirical evidential knowledge for the priority over construction complexities in a developing country (Kuwait) in the Middle East.

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.

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.

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 study is to investigate the influence that technological, environmental and organizational factors have on the rate of Internet of Things (IoT) adoption within the logistics industry. In addition, the moderating effect that the risk factor has on the technological, environmental and organizational factors regarding the implementation of IoT in logistics.

For the purpose of testing the models and hypotheses, a survey was carried out in order to collect the responses from currently employed individuals at various companies working in the field of logistics or IoT. For the purpose of analysis, the authors made use of the partial least squares structure equation model (PLS-SEM) technique.

Findings of this study concluded that technology- and environmental-related factors significantly affect the adoption of IoT in logistics, while risk acts as a moderator for the technological-related factor only in the adoption of IoT in logistics.

The relevance of the authors' study lies in the growing importance of IoT in logistics and the need for logistics companies to understand the factors that impact the adoption of IoT in their operations. By identifying and analyzing the factors that influence IoT adoption in logistics, the authors' study provides valuable insights that can help logistics companies make informed decisions about whether and how to adopt IoT.

The research will help organizations make strategies for the successful adoption of IoT and ease the lives of all the stakeholders.

In this research, the authors attempted to find the factors that influence the adoption of IoT in logistics management. The influence of the technological, environmental, organizational and risk-related factors on the adoption of IoT in logistics management was studied. The moderating effect of risk over these factors on the adoption of IoT in logistics was also analyzed. This is original work and has never been done earlier.

This study aims to investigate the critical success factors of digital transformation in the construction industry and identify whether the respondents' profiles influence their perceptions of critical success factors for digital transformation.

To achieve the objectives, a literature review was first conducted based on technology-organization-environment (TOE) framework. Then a questionnaire survey was carried out. A total of 86 people were surveyed in this study, mainly from the construction industry. At the level of data processing, SPSS was used for analysis. Among the main tests used were the Shapiro–Wilk test, reliability analysis, mean rank analysis, Kruskal–Wallis test and Mann–Whitney U test.

The study identified 15 critical success factors of digital transformation and found the three most important factors of digital transformation. Furthermore, respondents with different years of experience, enterprises with different sizes and different years made no difference in the perception of factors. Respondents' different occupations and types of enterprises created a bias in the perception of factors for digital transformation.

Firstly, the small sample size of the questionnaire limits the reference value of data analysis for certain groups. In addition, this study focuses broadly on construction enterprises without specifically examining different types of enterprises, thus lacking depth in its findings.

This study establishes a connection between TOE theory and the construction industry through an extensive literature review, identifying relevant factors and providing a reference for future research.

The study's results would enrich the research on digital transformation in the construction industry and provide a reference for the digital transformation of construction enterprises.

Project complexity is a critical issue that has increasingly attracted attention in both academic and practical circles. However, there are still many gaps in the research on project complexity, such as the differentiated conceptualization of complexity and disjointed operationalization in the measurements. Therefore, this paper aims to conduct a systematic and detailed literature review on the concept, dimensions, assessment, and underlying mechanisms of project complexity.

A systematic literature review methodology was applied to search and synthesize the research on project complexity, and a final sample of 74 journal articles was identified.

This study first summarizes the concepts of project complexity from three different theoretical perspectives, and then identifies different approaches of measurement, evaluation, or simulation to assess project complexity. This paper finally establishes an integrative framework to synthesize the antecedents, mediators and moderators, and outcomes of project complexity, generating four suggestions for future research.

This study summarizes the definition and operationalization of project complexity to reduce the discrepancies in the existing research and offers an integrative framework to offer a broad overview of the current understanding of project complexity, providing a potential way forward for addressing project complexity.

The purpose of this research is to develop a project complexity index (PCI) model using the best and worst method (BWM) to quantitatively analyze the impact of project complexities on the performance of metro rail projects.

This study employed a two-phase research methodology. The first phase identifies complexities through a literature review and expert discussions and categorizes different types of complexities in metro rail projects. In the second phase, BWM, a robust multi-criteria decision-making (MCDM) technique, was used to prioritize key complexities, and a PCI model was developed. Further, the developed PCI was validated through case studies, and sensitivity analysis was performed to check the accuracy and applicability of the developed PCI model.

The analysis revealed that location complexity exerted the most substantial influence on project performance, followed by environmental, organizational, technological and contractual complexities. Sensitivity analysis revealed the varying impacts of complexity indices on the overall project complexity.

The study's findings offer a novel approach for measuring project complexity's impact on metro rail projects. This allows stakeholders to make informed decisions, allocate resources efficiently and plan strategically.

The existing studies on project complexity identification and quantification were limited to megaprojects other than metro rail projects. Efforts to quantitatively study and analyze the impact of project complexity on metro rail projects are left unattended. The developed PCI model and its validation contribute to the field by providing a definite method to measure and manage complexity in metro rail projects.