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This study aims to develop a comprehensive framework for addressing delays in tunnel construction projects by leveraging predictable risk factors. Tunnel projects often encounter scheduling delays due to inherent complexities and uncertainties, necessitating a proactive approach to prevent project underperformance.

The integrated risk prioritization and determination of activity-wise delay (IRPAD) framework is divided into four phases: identification and prioritization of risk factors, determination of activity-wise risk coefficients using MCDM-based methodology, obtaining the critical risk path, and developing an activity-wise risk matrix. Fault tree analysis (FTA) and event tree analysis (ETA) are employed to determine activity-wise risk coefficients based on expert responses.

The framework’s applicability in Indian tunnel projects is demonstrated through a real-world case study with 95% validation accuracy. The IRPAD framework enhances the delay analysis process and facilitates the provision of effective activity-wise mitigation measures.

The IRPAD framework predicts delays in infrastructure projects thus enhancing resilience and sustainability, supporting SDGs 9 and 11. It can be applied to a wide range of construction projects to improve project performance.

This research introduces novel concepts such as the three fold activity-wise risk matrix and the critical risk path, contributing to the development of the IRPAD framework for delay reduction. This framework offers valuable insights to practitioners in the construction industry.

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.

Women’s employment has been a contentious issue in the Arab world. However, in today’s changing work conditions, workplaces, and the growth in education, women have increasingly occupied higher-level roles, and their presence in diverse industries has expanded. The study takes a holistic approach to female labor force participation (FLFP) in Oman, encompassing various theories and factors influencing women’s engagement in private sector jobs.

The research employed an exploratory design followed by a qualitative analysis based on a constructivist grounded theory approach. The study involved 170 semi-structured face-to-face interviews with women workers in Oman’s private sector. It explored work–family conflict (WFC), work–life balance (WLB), organizational and family support, and societal bias impacting female labor force participation.

The low FLFP in Oman’s private sector is a complex issue shaped by various interconnected challenges. Our qualitative analysis synthesizes a conceptual framework that highlights WFC and WLB as pivotal factors behind low FLFP, as women in the private sector face long work hours, low salaries, and high responsibilities. Our study confirms that these women receive substantial familial support in dealing with WFC but lack organizational support and family-friendly policies (FFPs).

Addressing the low participation of women in Oman’s private sector labor force necessitates comprehensive strategies encompassing legislative measures, cultural shifts, and organizational reforms. Implementing these strategies creates an environment where women feel empowered to participate and actively thrive in the private sector workforce.

This study indicates the presence of some serious social issues, such as society’s bias, lack of support for working women, and the gender gap in the private sector workforce, which has broad implications and significance for Oman. The study indicates the positive role of support women receive from their families, organizations, and Oman labor law and what could improve their participation further.

This study addresses the limited research on challenges encountered by women in Oman’s private-sector workforce. Our research addresses this gap by answering key questions, collecting and interpreting data, and developing a comprehensive conceptual framework. This framework aims to elucidate the factors contributing to women’s reluctance to pursue private-sector employment, considering the diverse issues they confront.

Managing project completion within the stipulated time is significant to all firms' sustainability. Especially for software start-up firms, it is of utmost importance. For any schedule variation, these firms must spend 25 to 40 percent of the development cost reworking quality defects. Significantly, the existing literature does not support defect rework opportunities under quality aspects among Indian IT start-ups. The present study aims to fill this niche by proposing a unique mathematical model of the defect rework aligned with the Six Sigma quality approach.

An optimization model was formulated, comprising the two objectives: rework “time” and rework “cost.” A case study was developed in relevance, and for the model solution, we used MATLAB and an elitist, Nondominated Sorting Genetic Algorithm (NSGA-II).

The output of the proposed approach reduced the “time” by 31 percent at a minimum “cost”. The derived “Pareto Optimal” front can be used to estimate the “cost” for a pre-determined rework “time” and vice versa, thus adding value to the existing literature.

This work has deployed a decision tree for defect prediction, but it is often criticized for overfitting. This is one of the limitations of this paper. Apart from this, comparing the predicted defect count with other prediction models hasn’t been attempted. NSGA-II has been applied to solve the optimization problem; however, the optimal results obtained have yet to be compared with other algorithms. Further study is envisaged.

The Pareto front provides an effective visual aid for managers to compare multiple strategies to decide the best possible rework “cost” and “time” for their projects. It is beneficial for cost-sensitive start-ups to estimate the rework “cost” and “time” to negotiate with their customers effectively.

This paper proposes a novel quality management framework under the Six Sigma approach, which integrates optimization of critical metrics. As part of this study, a unique mathematical model of the software defect rework process was developed (combined with the proposed framework) to obtain the optimal solution for the perennial problem of schedule slippage in the rework process of software development.

Despite a growing interest in investigating green skepticism, there has been a paucity of effort in synthesizing green skepticism research. This study aims to synthesize extant green skepticism research. This study is useful in providing a comprehensive synthesis of research on green skepticism and identifying key gaps in the existing literature, thereby paving the way for future research directions. This research demonstrates a comprehensive understanding of the dominant theories, contexts (i.e. countries), characteristics (i.e. key variables and their associations) and methods (i.e. research design) employed in green skepticism research.

A hybrid review method that combined an Systematic Literature Review (SLR) with lexicometric analysis was employed to present a comprehensive synthesis of the green skepticism literature.

The findings showed that many theories have been applied to green skepticism research with the theory of planned behavior and attribution theory being prominently used. The review also proposes a conceptual framework that illustrates the relationships between antecedents and consequences reported in the green skepticism literature. Lexicometric analysis identified six major themes in green skepticism research, including trust in green products, product-related attributes, environmental attributes, social responsibility, organic consumption and promotional campaigns.

This review included only papers authored in English. Consequently, studies conducted in other languages were not taken into account. Future research could broaden the understanding of green skepticism by incorporating studies conducted in different languages.

This study provides practical implications for retailers, marketers and policymakers to combat green skepticism and encourage the purchase of green items. This study suggests integrating endorsements from authorized third-party organizations or celebrities into green marketing campaigns to reduce skepticism. Further, aligning corporate social responsibility efforts with genuine social welfare goals can build consumer trust. Finally, securing third-party endorsements for ecological claims will emphasize product credibility and drive green purchase intention.

To the authors’ knowledge, this study represents the first attempt to review the extant literature in green skepticism by integrating a systematic review approach, lexicometric analysis and the Theory-Context-Characteristics-Methodology framework. Through this approach, (1) SLR provides a detailed synthesis of green skepticism research to date, (2) Lexicometric analysis aids in the identification of key themes investigated in green skepticism research to date, and (3) the TCCM framework assists in identifying crucial avenues for future research endeavors in the realm of green skepticism.

Liquidity is a primary concern for businesses. The purpose of this study is to understand the impact of the collaborative liquidity management within the supply chain. Larger firms prescribe favorable trade terms in the transactions and do not engage in value chain vision sharing with their smaller counterparts. Smaller firms encounter challenges with liquidity and often face the risk of bankruptcy. Such practice can threaten the entire supply chain. Instead, collaborative liquidity management can offer a win–win scenario to both parties. In that case, what are the benefits of implementing a collaborative liquidity management approach across the value chain, and what is the reward?

The authors selected key liquidity metrics that matter most to the organizations from a cohort of 307 firms from the Indian automobile industry for 10 years (2012–2021). The authors classified the businesses into five distinct revenue-based categories. They emphasized the importance of expanded supply chain finance adoption and demonstrated how collaborative liquidity management strategies boosted return on assets.

The research confirms the tangible benefits of greater adoption of supply chain finance in realizing supply chain members’ shared vision. The authors challenged the age-old practice of power-based relationships in the supply chain. They recommended a win–win scenario through practical cooperation and increased adoption of SCF by value chain members.

Existing research predominantly focuses on dyadic relationships and is restricted to Europe and China. According to the authors, no comprehensive investigation has been conducted in India. This application of simulation techniques to improve the liquidity performance of companies in developing economies is innovative.

The study explores the potential barriers to data science (DS) implementation in organizations and identifies the key barriers. The identified barriers were explored for their interconnectedness and characteristics. This study aims to help organizations formulate apt DS strategies by providing a close-to-reality DS implementation framework of barriers, in conjunction with extant literature and practitioners' viewpoints.

The authors synthesized 100 distinct barriers through systematic literature review (SLR) under the individual, organizational and governmental taxonomies. In discussions with 48 industry experts through semi-structured interviews, 14 key barriers were identified. The selected barriers were explored for their pair-wise relationships using interpretive structural modeling (ISM) and fuzzy Matriced’ Impacts Croise's Multiplication Appliquée a UN Classement (MICMAC) analyses in formulating the hierarchical framework.

The lack of awareness and data-related challenges are identified as the most prominent barriers, followed by non-alignment with organizational strategy, lack of competency with vendors and premature governmental arrangements, and classified as independent variables. The non-commitment of top-management team (TMT), significant investment costs, lack of swiftness in change management and a low tolerance for complexity and initial failures are recognized as the linkage variables. Employee reluctance, mid-level managerial resistance, a dearth of adequate skills and knowledge and working in silos depend on the rest of the identified barriers. The perceived threat to society is classified as the autonomous variable.

The study augments theoretical understanding from the literature with the practical viewpoints of industry experts in enhancing the knowledge of the DS ecosystem. The research offers organizations a generic framework to combat hindrances to DS initiatives strategically.

The purpose of this study is to evaluate the impact of green human resource management (GHRM) practices on Indian companies of different sectors and to identify the most critical GHRM practices that can lead to a more sustainable and environmentally friendly workplace.

This study uses an integrated Analytic Hierarchy Process-Evaluation based on Distance from Average Solution approach to determine the importance of 32 GHRM practices classified into eight categories, as identified through literature review and expert consultation. This study also identifies the best sector for GHRM practices in India.

This study reveals that employee engagement is the most critical practice among all the GHRM practices identified. India’s Information Technology-Enabled Services sector benefited the most from GHRM practices, followed by the Insurance sector.

This study contributes to the literature on GHRM practices and their impact on organisations and sectors. The integrated Analytic Hierarchy Process-Evaluation based on Distance from Average Solution approach used in this study is innovative and can be helpful for Indian companies to prioritise and implement effective GHRM practices.

The United Arab Emirates (UAE) offers a level playing field to all ethnic entrepreneurs (EE’s) operating from within it. The purpose of this qualitative research case study is to explore the reasons underpinning the relatively greater success that Indian-origin EE’s in the UAE have been enjoying for sustained periods – and across diverse industries – relative to their counterparts belonging to several other nations.

Qualitative research case study that draws from data gathered through 30 interviews of participants identified through expert sampling.

Whereas the UAE treats all its ethnic entrepreneurs (EE’s) alike and provides them with a level platform to operate from, the EE’s from India have consistently been outperforming those from all other nations, particularly within the context of the UAE’s large businesses spanning diverse industries. Three features seem to explain their success: their high tolerance for ambiguity; thriftiness; and intercultural competence.

Two key limitations were faced: firstly, the negligible research literature on ethnic entrepreneurship in the UAE, and related official statistics such as details (by ethnicity/nationality) of EE-owned businesses, and secondly, the industry-wise break down of such businesses and their performance, as available in other developed nations hosting EE’s. Resultantly, alternate sources of data have been used to complete this research.

Given the UAE’s national-level institutionalised efforts to promote entrepreneurship amongst its citizens and wider populace, there are many implications that this study holds for existing and future entrepreneurs.

While on the one hand, the UAE and the wider Gulf Cooperation Council region have been witnessing frenetic ethnic entrepreneurial activity in the past decade, the research literature on the regions’ ethnic entrepreneurship is extremely patchy. This case study serves to significantly bridge this gap, and to the best of the authors’ knowledge, this is the first work, that extensively explores the entrepreneurial trajectory of Indian EE’s in the UAE, and the factors driving their success.