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Managing mega infrastructure projects (MIPs) is more complex because of time, size, social, environmental and financial implications. This study aims to address the management approaches, complexity and risk factors involved in MIPs. The study focuses on project success criteria and their individual effects on the success of MIPs.

To address the challenges and identify the most influencing factor for the success of MIPs, the study deployed a cross-sectional survey approach. Six hundred eighty-two usable samples were collected from the respondents to understand the impact of predetermined factors on the success of MIPs. The structural equation model and artificial neural network approach were used to derive the importance of factors affecting the success of MIPs.

The study's outcome confirms that all three influencing factors: feasibility studies, community engagements and contract selection, have a significant positive impact on the success of MIPs. Community engagement amongst all three has the most influential predictor for the success of MIPs.

The developed model will enable practitioners and policymakers from Indian construction companies and other emerging nations to concentrate on recognized risk reduction variables to enhance project success criteria and project management success, especially for MIPs.

The purpose of this research is to address the complex multiobjective unrelated parallel machine scheduling problem with real-world constraints, including sequence-dependent setup times and periodic machine maintenance. The primary goal is to minimize total tardiness, earliness and total completion times simultaneously. This study aims to provide effective solution methods, including a Mixed-Integer Programming (MIP) model, an Epsilon-constraint method and the Nondominated Sorting Genetic Algorithm (NSGA-II), to offer valuable insights into solving large-sized instances of this challenging problem.

This study addresses a multiobjective unrelated parallel machine scheduling problem with sequence-dependent setup times and periodic machine maintenance activities. An MIP model is introduced to formulate the problem, and an Epsilon-constraint method is applied for a solution. To handle the NP-hard nature of the problem for larger instances, an NSGA-II is developed. The research involves the creation of 45 problem instances for computational experiments, which evaluate the performance of the algorithms in terms of proposed measures.

The research findings demonstrate the effectiveness of the proposed solution approaches for the multiobjective unrelated parallel machine scheduling problem. Computational experiments on 45 generated problem instances reveal that the NSGA-II algorithm outperforms the Epsilon-constraint method, particularly for larger instances. The algorithms successfully minimize total tardiness, earliness and total completion times, showcasing their practical applicability and efficiency in handling real-world scheduling scenarios.

This study contributes original value by addressing a complex multiobjective unrelated parallel machine scheduling problem with real-world constraints, including sequence-dependent setup times and periodic machine maintenance activities. The introduction of an MIP model, the application of the Epsilon-constraint method and the development of the NSGA-II algorithm offer innovative approaches to solving this NP-hard problem. The research provides valuable insights into efficient scheduling methods applicable in various industries, enhancing decision-making processes and operational efficiency.

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.

Members in the international joint ventures (IJVs) for high-speed rail (HSR) projects usually engage in coopetition interactions to create common benefits (CB) and simultaneously safeguard private benefits (PB). Previous studies of coopetition and performance using variance-based methods usually ignore the combinational influence of diverse coopetition constructs on performance, which can be effectively compensated by adopting a configuration perspective. Therefore, this research aims to ascertain various combinations of three coopetition constructs (coopetition relationship, coopetition capability and coopetition strategy) that lead to high IJVs’ performance through a configuration approach.

First, the research framework of coopetition configuration was established, and the key constructs were operationalized, which were validated by expert interviews. Then the information on 12 HSR IJVs was collected and quantified through nine rounds of interviews and a questionnaire survey. Later, the fuzzy-set qualitative comparative analysis (fsQCA) was applied to explore what coopetition configurations benefit the CB or PB achievement.

Configuration results indicate that six coopetition configurations lead to CB outcome and seven configurations lead to PB outcome. Based on the results, coopetition contexts are divided into four categories: firm-based coopetition, project-based coopetition, firm-project-based coopetition and none-based coopetition. Then, a selection scheme for coopetition strategies in various contexts has been developed. The results also show that the core conditions mostly appear in the coopetition relationships and coopetition strategies dimensions, and the optimal coopetition strategies vary in different contexts.

This study enhances the theoretical understanding of coopetition in HSR IJVs and assists relative HSR industrialists, as well as the mega infrastructure project managers, in IJVs’ implementation. The configuration perspective of this paper also contributes to a systemic and holistic view of coopetition in HSR IJVs.

This study aims to investigate how configurations of boundary objects (BOs) support innovation teams in developing innovative product concepts. Specifically, it explores the effectiveness of different artefact configurations in facilitating collaboration and bridging knowledge boundaries during the concept development process.

The research is based on data from ten undergraduate innovation teams working with an industry partner in a creative industry. Six categories of BOs are identified, which serve as tools for collaboration. The study applies fsQCA (fuzzy-set qualitative comparative analysis) to analyse the configurations employed by the teams to bridge knowledge boundaries and support the development of innovative product concepts.

The findings of the study reveal two distinct groups of configurations: product envisioning and product design. The configurations within the “product envisioning” group support the activities of visioning and pivoting, enabling teams to innovate the product concept by altering the product vision. On the other hand, the configurations within the “product design” group facilitate experimenting, modelling and prototyping, allowing teams to design the attributes of the innovative product concept while maintaining the product vision.

This research contributes to the field of innovation by providing insights into the role of BOs and their configurations in supporting innovation teams during concept development. The results suggest that configurations of “product envisioning” support bridging semantic knowledge boundaries, while configurations within “product design” bridge pragmatic knowledge boundaries. This understanding contributes to the broader field of knowledge integration and innovation in design contexts.

This research examines the effect of team diversity on customer behavior (purchase likelihood) associated with sustainable luxury products and further considers the mediating role of customer skepticism and the moderating role of the growth mindset in these relationships.

Study 1 aims to confirm the direct effect of team diversity on purchase intention and the mediating effect of customer skepticism. Featuring a fictitious brand, Study 2 seeks to test the moderating effects of a growth mindset. This research recruits participants located in the USA who have shopping experiences with a luxury product.

The findings support the notion that team diversity can mitigate customers' skepticism while enhancing purchase likelihood. Moreover, this effect is stronger among those with a growth mindset. As such, the findings suggest that communicating the heterogeneous composition of team members can benefit sustainable luxury brands.

Underpinned by the signaling theory and incremental theory, this research examines the effects of team diversity on customer behavior (purchase likelihood) related to sustainable luxury products, as well as the role of customer skepticism (as a mediator) and a growth mindset (as a moderator) in these relationships. Thus, the findings broaden the current diversity research which has predominantly focused on team decision-making and performance.

With the advancement of digital transformation, it is important for e-retailers to use artificial intelligence (AI) for customer engagement (CE), as CE enables e-retail brands to succeed. Essentially, AI e-marketing (AIeMktg) is the use of AI technological approaches in e-marketing by blending customer data, and Retail 4.0 is the digitisation of the physical shopping experience. Therefore, in the era of Retail 4.0, this study investigates the factors influencing the use of AIeMktg for transforming CE.

The primary data were collected from 305 e-retailer customers, and the analysis was performed using a quantitative methodology.

The results reveal that AIeMktg has tremendous applications in Retail 4.0 for CE. First, it enables marketers to swiftly and responsibly use data to anticipate and predict customer demands and to provide relevant personalised messages and offers with location-based e-marketing. Second, through a continuous feedback loop, AIeMktg improves offerings by analysing and incorporating insights from a 360-degree view of CE.

The main contribution of this study is to provide theoretical underpinnings of CE, AIeMktg, factors influencing the use of AIeMktg, and customer commitment in the era of Retail 4.0. Subsequently, it builds and validates structural relationships among such theoretical underpinning variables in transforming CE with AIeMktg, which is important for customers to expect a different type of shopping experience across digital channels.

Purpose: The study focusses on the systematic review of the greenwashing literature to present the research gaps to researchers for future studies.

Design/methodology/approach: The systematic review has been used to analyse past studies on ‘greenwashing’. The 325 research articles of the previous 10 years (2014–2023) were downloaded from the Scopus-indexed database using the keyword ‘greenwashing’.

The findings: There is a need for a study on greenwashing in developing countries like India. An attempt should be made to analyse the research with a large sample size.

Research limitations: The scope of the data used in this study is limited by the source of retrieval, that is, the Scopus. The current source adequately serves the study’s purpose, as the Scopus database is one of the most significant citation databases. This study analyses data from the years 2014 to 2023 to improve credibility and reduce biases.

Practical implications: The research findings will significantly help researchers, green marketers, and practitioners be aware of the emerging markets of greenwashing and consumers’ rising greenwashing perception of green products.

Originality/value: This study is a novel attempt to explore a better understanding of greenwashing for researchers. The study is original; work has yet to be performed on this topic.

Co-creation of services (CCOS) is a collaborative strategy that emphasises customer involvement and their expertise to increase the value of the service experience. In the service ecosystem, artificial intelligence (AI) plays a key role in value co-creation. Therefore, this study is undertaken to empirically uncover how AI can empower CCOS.

The source data were collected from 305 service provider respondents and quantitative methodology was applied for data analysis.

New service development augmented with AI provides tangible value to service providers while also providing intangible value to supportive customers. With AI, service providers adapt to new innovations and enrich additional information, which eventually outperforms human-created services.

AI adoption for CCOS empowerment in service businesses brings “service-market fit”, which represents the significant benefits wherein customers contribute to creativity, intuition, and contextual awareness of services, and AI contributes to large-scale service-related analysis by handling volumes of data, service personalisation, and more time to focus on challenging problems of the market.

This study presents theoretical concepts on AI-empowered CCOS, AI technological innovativeness, customer participation in human-AI interaction, AI-powered customer expertise, and perceived benefits in CCOS, and subsequently discusses the CCOS empowerment framework. Then, it proposes a novel conceptual model based on the theoretical concepts and empirically measures and validates the intention to adopt AI for CCOS empowerment. Overall, the study contributes to novel insight on empowering service co-creation with AI.

Although the needs and desires of customers are different, obtaining positive and negative experiences is an inevitable consequence of consuming products and services purchased by the consumer. The nature of these experiences is very important for the retail industry and in particular sports stores, as the intangible experience includes the main essence of the products and services provided by businesses; in other words, sports products are mostly experience-oriented and can be classified as an overall experience. Despite the importance of the consumer experience as a dominant and effective paradigm in consumer behaviour, the design of sports customer experience has received less attention from researchers. Therefore, the present study seeks to answer the question of what aspects of creating and developing the customer experience in sports and recreational complexes are. To answer this question, this chapter attempts to identify the areas of creating customer experiences in sports and recreational environments by examining the case of the Enghelab sports and recreational club (in Tehran, Iran) using qualitative research methods and interviewing experts in this field and using the content analysis technique. The findings indicate that cognitive, functional, comprehensive, human, physical, functional, aesthetic, sensory, social, emotional and communal components are the most important components of creating and enhancing customer experience in sports and entertainment complexes.

Therefore, the owners, managers and marketing unit of these collections should invest in the creation and development of these components in the design of their strategic marketing plans in order to provide a memorable experience for the customer during their journey and finally be able to benefit from the subsequent positive consequences such as customer satisfaction, word-of-mouth advertising, loyalty and return intention.