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The increasing prevalence of a wide range of infectious diseases, as well as the underwhelming results of vaccination rates that may be traced back to problems with vaccine procurement and distribution, have brought to the fore the importance of vaccine supply chain (VSC) management in recent years. VSC is the cornerstone of effective vaccination; hence, it is crucial to enhance its performance, particularly in low- and middle-income countries where immunization rates are not satisfactory.

In this paper, the authors focus on VSC performance improvement of India by proposing supply contracts under demand uncertainty. The authors propose three contracts – wholesale price (WSP), cost sharing (CS) and incentive mechanism (IM) for the government-operated immunization program of India.

The authors' findings indicate that IM is capable of coordinating the supply chain, whereas the other two contracts are inefficient for the government. To validate the model, it is applied to a real-world scenario of coronavirus disease 2019 (COVID-19) in India, and the findings show that an IM contract improves the overall efficiency of the system by 23.72%.

Previous studies focused mainly on the influenza VSC industry within developed nations. Nonetheless, there exists a dearth of literature pertaining to the examination of supply contracts and their feasibility for immunization programs that are administered by the government and aimed at optimizing societal benefits. The authors' findings can be beneficial to the immunization program of India to optimize their VSC cost.

The coronavirus (COVID-19) is the worst pandemic in recent memory in terms of its economic and social impacts. Deadly second wave of COVID-19 in India shook the country and reshaped the ways organizations functions and societies behave. Medical infrastructure was unaffordable and unsupportive which created high distress in the Indian society, especially for poor. At this juncture, some pharmaceutical firms made a unique social investment when they reduced price of drugs used to treat COVID-19 patients. This study aims to examine how the market and the society respond to the price reduction announcement during the psychological distress of COVID-19.

Market reactions have been analyzed by conducting an event study on stock market data and visual analytics-based sentiment analysis on Twitter data.

Overall, this study finds positive abnormal returns on the day and around the day of event. Interestingly, this study finds that returns during the time of high distress are significantly higher. Sentiment analysis conveys that net sentiment is favorable to the pharmaceutical firms around the day of event and it sustains more during the time of high distress.

This study is unique in contributing to the business and industrial management literature by highlighting market reactions to social responsibility of business during the time of psychological distress in emerging economies.

This study aims to investigate the various systems in logistics industry of Pakistan through the lens of the World Bank's logistics performance indicators (LPI) and understand their impact on the China–Pakistan economic corridor (CPEC) that is a vital part of China's belt and road initiative (BRI).

In this study thematic analysis was performed on twenty-three semi-structured interviews with experts in Pakistan's logistics and supply chain sector to gain an in-depth insight into the logistics performance relative to CPEC.

A performance gap exists in the logistics systems in Pakistan, both for hard and soft infrastructure. The significant challenges are the inefficiencies of the government, minimal use of information and computing technology (ICT), and an incapable workforce. It is essential to be cognizant of the ground realities and amendments required in the existing policies and practices in light of the challenges faced and best practices adopted by developed and developing countries with good standing in logistics performance. This study will guide policymakers and practitioners for hard and soft logistics infrastructure improvement, which may benefit economic corridors in general and CPEC in particular.

This study contributes to the existing literature by highlighting the role of ICT in improving both soft and hard logistics infrastructure, which can lead to significant development of economic corridors. The study makes use of a case study of the CPEC to demonstrate the lack of ICT can hamper the growth of an economic corridor despite billions of dollars of investment in the hard infrastructure development projects.

Drawing on organizational learning and dynamic capabilities literature, this study aims to explore how suppliers from traditional emerging economies (STEE) can acquire, assimilate and use new knowledge essential for the development of production and marketing capabilities. These capabilities then facilitate suppliers in climbing the value chain from B-to-B to B-to-C.

The study adopted a longitudinal and multiple case study design to examine the practices of suppliers operating in a traditional emerging economy setting. This study selected Pakistan textile industry as an empirical setting, which is a predominantly supplier market for global buyers. Data sources entail semi-structured interviews with top executives and senior-level managers in four case firms and secondary data obtained from diverse sources.

The study identified transitionary phases of capabilities development that are facilitated by boundary-spanning knowledge acquisition and transformation in a dynamic manner. These capabilities are essential for a supplier’s entry into downstream international markets (i.e. launching its own products/brands in the end consumers’ market).

The findings could help managers in STEEs to understand the strategic importance of supply chain ties in their learning and capabilities development. It also provides strategic insights on what, how and why involved parties do engage over an extended period of time. Moreover, the findings of this study could help other firms to know and adopt the right type of technology(s) and systems that can help them reduce the technological gap in producing and marketing market-winning products.

This study advances the recent academic discussion that focusses on learning by supplying and the value-chain movement of suppliers (i.e. B-to-C) from their B-to-B engagements. The findings identified the vital and beneficial role of long-term relationships with global value chain partners in learning and capabilities development that led to value creation in the traditional emerging economy.

The supply chain (SC) of the fast-moving consumer goods (FMCG) sector in India witnessed a significant change soon after introducing the Goods and Services Tax (GST). With the initiation of this tax, companies started moving from individual state-wise warehouses to consolidation warehouses model to save costs. This paper proposes a model that frames a mathematical formulation to optimize the distribution network in the downstream SC by considering the complexities of multi-product lines, multi-transport modes and consolidated warehouses.

The model is designed as mixed-integer linear programming (MILP), and an algorithm is developed that works on the feedback loop mechanism. It optimizes the transportation and warehouses rental costs simultaneously with impact analysis.

Total cost is primarily influenced by the critical factor transportation price rather than the warehouse rent. The choice of warehouses at prime locations was a trade-off between a lower distribution cost and higher rent tariffs.

The study enables FMCG firms to plan their downstream SC efficiently and to be in line with the recent trend of consolidation of warehouses. The study will help SC managers solve complexities such as multi-product categories, truck selection and consolidation warehouse selection problems and find the optimum value for each.

The issues addressed in the proposed work are transporting products with different sizes and weights, selecting consolidated warehouses, selecting suitable vehicles for transportation and optimizing distance in the distribution network by considering consolidated warehouses.

Supply chain vulnerability (SCV) analysis is vital for manufacturers globally because it creates a pathway for building resilient supply chains in uncertain environments. This study aims to identify drivers of SCV in the Indian manufacturing sector.

Sixteen drivers were identified from the literature review and followed by expert interviews. Interpretive structural modeling was used to determine the hierarchical structural relationship among identified SCV factors.

It was found that risk is not a board room agenda. Misaligned performance measures with incentives and lack of risk dashboard are the causal factors of SCV. Supply chain security, centralized production and distribution and lack of trust in the supply chain were driven factors.

This provides new insights to assess and prioritize initiatives for supply chain sustainability in terms of continuing business operations. The structural model provides a systemic view of SCV and helps reduce vulnerability.

As public–private partnerships (PPPs) have become preferred and veritable approach to deliver affordable housing, the seemingly lack of understanding of the significant factors that impact on success has become a notable setback. This study aims to delineate significant factors that can support decisions in affordable PPP public housing delivery.

Largely, a questionnaire survey was adopted to elicit insights from practitioners, policymakers and experts to develop an evaluative decision support model using an analytical hierarchy process and multi-attribute utility technique approach. Further, an expert illustration was conducted to evaluate and validate the results on the housing typologies.

The results revealed that energy efficiency and low-cost green building materials scored the highest weighting of all the criteria. Furthermore, multi-storey self-contained flats were found to be the most preferred housing typology and were significantly influenced by these factors. From the model evaluation, the scores on the factors of sustainability, affordability, cultural values and accountability were consistent across all typologies of housing whereas that of benchmarking, governance and transparency were varied.

The decision support factors captured varied dimensions of key factors that impact on affordable PPP housing that have not been considered in an integrated manner. These findings offer objective and systematic support to decision-making in affordable PPP housing delivery.

This paper aims to theorize and investigate the use of effective color features in artificial intelligence (AI) influencers, an emerging marketing trend in the social media context.

By analyzing 6,132 pictures posted by ten AI influencers on Instagram, this paper examines the effect of warm colors in AI influencers’ social media posts on consumer responses, and how other color features may moderate the effect of warm color. In addition, two experimental studies reveal the underlying process driving the effect of warm color.

Warmer color generated more favorable consumer responses, with brightness significantly moderating the relationship between warm color and favorable consumer responses. Moreover, the results of the experiments establish that perceived warmth and emotional trust mediate the causal effect of warm colors on consumer responses.

There is still little understanding about consumer perceptions of AI influencers and their acceptance of AI influencers’ product recommendations. As such, this research offers theoretical understanding of the color features influencing the effectiveness of recommendations by AI influencers.

Brands have started deploying AI influencers as their brand ambassadors to make product recommendations, representing a new wave of advertising on social media. The findings will thus benefit marketers in developing effective product recommendations using AI influencers.

The present research provides a novel understanding of how visual features, such as color can influence the effectiveness of AI influencers.

Mothers play important roles in their families' lives. When they are high performance athletes, they need specific supports that will enable them to excel in their roles as mother athletes. The feminist qualitative research in this chapter is based on data from two studies drawn from semi-structured interviews with elite female distance runners: 14 in 2013–2014 and 11 in 2021. We address two questions: (1) what are the considerations that elite female distance runners make around planning their pregnancy(ies) and family lives? and (2) how have experiences shifted between athlete interviews in 2013–2014 and a new cohort of athletes in 2021? In order to address these questions, we drew on three complementary theoretical approaches: liberal feminism, radical feminism, and strategic essentialism. Further, we then used thematic analysis and generated three broader themes about elite female distance runners that aligned with both cohorts of athletes. First, athletes are forced to plan/strategize their pregnancies around finances, competitions, contracts, and spousal supports due to the lack of support from athletic governing bodies or corporate sponsors. Second, female athletes who choose to have children experience stress and uncertainty in their athletic careers that their male counterparts do not. Third, elite female athletes are demanding that further change occur to address these inequalities, and participants offered a number of potential solutions to improve supports for these athletes. Although solid progress has been noted in the timeframes of our two cohorts, further commitment from athletic governing bodies and corporate sponsors is needed to work toward gender equity in athletics.

This study aims to develop a hierarchical reliability framework to evaluate the service delivery performance of education public–private partnerships (PPPs) effectively and efficiently during long-term operations.

The research design included development and test phases. In the development phase, three performance layers, i.e. indicator, component and system, in the education service delivery system were identified. Then, service component reliability was computed through first order reliability method (FORM). Finally, the reliability of the service system was obtained using dynamic component weightings. A PPP school example in Australia was set up in the test phase, where performance indicators were collected from relevant contract documents and performance data were simulated under three assumptive scenarios.

The example in the test phase yielded good results for the developed framework in evaluating uncertainties of service delivery performance for education PPPs. Potentially underperforming services from the component to the system level at dynamic timepoints were identified, and effective preventative maintenance strategies were developed.

This research enriches reliability theory and performance evaluation research on education PPPs. First, a series of performance evaluation indicators are constructed for assessing the performance of the service delivery of the education PPP operations. Then, a reliability-based framework for service components and system is developed to predict service performance of the PPP school operations with consideration of a range of uncertainties during project delivery.

The developed framework was illustrated with a real-world case study. It demonstrates that the developed reliability-based framework could potentially provide the practitioners of the public sector with a basis for developing effective preventative maintenance strategies with the aim of prolonging the service life of the PPP schools.

Evaluating education PPPs is challenging as it involves long-term measurement of various service components under uncertainty. The developed reliability-based framework is a valuable tool to ensure that reliability is maintained throughout the service life of education PPPs in the presence of uncertainty.