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This study aims to examine the impact of disclosure on the unsolved initial public offering (IPO) puzzle. For this purpose, the authors analyzed the impact of the primary uses of the proceeds disclosed in a firm's IPO prospectus on underpricing, prelisting performance, postlisting underperformance and operating performance.

This study uses Indian public firms that went public between March 31, 2010, and March 31, 2020. A multivariate regression technique was used to study the impact of the primary uses of proceeds on underpricing, prelisting performance and postlisting underperformance, whereas a quantile regression technique was used to study their impact on operating performance.

The authors found that the primary use of proceeds disclosure helps predict underpricing and returns to the investor only until day 60 postlisting; beyond that, they provide no further insights into the firm's performance. Firms with lower and average operating performance should not state the general corporate purposes and payment on borrowings, respectively, as their primary use of proceeds, as it leads to a decline in their operating performance.

Results might suffer from the potential endogeneity problem due to selection bias. This research focuses on India only, which makes generalization of results for other economies difficult. Future research may extend the post-IPO period and include more developing economies. Furthermore, future studies can draw comparisons between developed and developing nations' disclosures of using proceeds.

This study will help the firms going public in India better disclose the use of proceeds based on their characteristics. Stating future acquisitions, payments on borrowings and working capital reduces the uncertainty, and therefore, these are feasible avenues for investing proceeds raised through IPO.

The authors used ten categories for the primary use of proceeds disclosure, whereas previous studies have used only five to six categories. To the best of the authors’ knowledge, this study was the first to use underpricing, postlisting performance and operating performance in a single study. These measures gave a more holistic view of the use of proceeds disclosure.

Synthetic biology begins with the underlying assumption that life and life forms can be divided into parts and reassembled or redesigned according to the whims of their creators. Therefore, synthetic biology needs to be at the centre of ethical thinking since it engages the very concept of life and radically changes it. In this paper, we will investigate the phenomenon of synthetic biology through an ethical analysis of the unfulfilled promises and potential perils surrounding this technology. The paper consists of four parts. In the first part, we will deal with the problem of defining synthetic biology since it is a field in which many scientific disciplines meet and intertwine. The second part will present a brief history of systemic biology and the groundbreaking creation of Synthia, the first synthetic organism. The third part focuses on synthetic biology's potential benefits and some prominent ethical issues. In the fourth part, we will point out the problem of synthetic biology regulation. In conclusion, we will highlight the essential ethical remarks on synthetic biology and provide the impetus for further ethical debate.

With an emphasis on spatial health disparities, this study examines how COVID-19 has affected healthcare access and inequality in India. The study developed the Healthcare Access Index (HAI) and Healthcare Inequality Index (HII) to assess the pandemic’s effects on healthcare. The study addresses spatial health disparities in healthcare access and inequality, filling gaps in the literature. The final aim of the study is to offer policy suggestions to lessen healthcare inequities in India, particularly in the context of COVID-19.

The study incorporates secondary data from publicly accessible databases such as the National Family Health Survey, Niti-Ayog and Indian Census databases and employs a quantitative research design. The impact of the COVID-19 pandemic on healthcare access and healthcare inequality in India is examined using the HAI and the HII. The five dimensions of healthcare access – availability, accessibility, accommodation, cost and acceptability – were used in developing the HAI. The study uses a panel data analysis methodology to examine the HAI and HII scores for 19 states over the pre-COVID-19 (2015) and post-COVID-19 (2020) periods. In order to investigate the connection between healthcare access, healthcare inequality and the COVID-19 pandemic, the analysis employs statistical tests such as descriptive statistics, correlation analysis, factor analysis and visualization analysis.

According to the study, COVID-19 impacted healthcare access and inequality in India, with notable regional inequalities between states. The pandemic has increased healthcare disparities by widening the gap between states with high and low HII ratings. Healthcare access is closely tied to healthcare inequality, with lower levels of access being associated with more significant levels of inequality. The report advises governmental initiatives to lessen healthcare disparities in India, such as raising healthcare spending, strengthening healthcare services in underperforming states and enhancing healthcare infrastructure.

For Indian healthcare authorities and practitioners, the study has significant ramifications. In light of the COVID-19 pandemic, there has been a main focus on addressing geographic gaps in healthcare access and inequality. The report suggests upgrading transportation infrastructure, lowering out-of-pocket costs, increasing health insurance coverage and enhancing healthcare infrastructure and services in underperforming states. The HAI and the HII are tools that policymakers can use to identify states needing immediate attention and appropriately spend resources. These doable recommendations provide a framework for lowering healthcare disparities in India and enhancing healthcare outcomes for all communities.

The study’s originality resides in establishing the HAI and HII indices, using panel data analysis and assessing healthcare inequality regarding geographic disparities. Policy choices targeted at lowering healthcare disparities and enhancing healthcare outcomes for all people in India can be informed by the study’s practical consequences.

After completion of the case study, students will be able to discuss the characteristics of sustainable enterprises driving the innovation; analyze the concept of waste to wealth, along with its associated benefits and challenges; provide an example of a sustainable start-up that operates conventionally and is attempting to increase production capacity through automation; and describe the strategies for scaling up the business.

Mr Manigandan Kumarappan’s goal was to provide the world with alternatives to plastic and other nonbiodegradable articles used in homes, offices, hotels and other places which compelled him to leave his corporate life behind and become an entrepreneur. His knowledge, expertise and creativity made him work toward providing a sustainable solution to the plastic-free world which made him create leafy straws for the world. His start-up Evalogia made 10,000 straws a day, mostly with manual production and machine-assisted in part of the processes. Evalogia got orders from all over the world after the ban on plastic from many countries. However, Evalogia was unable to meet the demand, as the manufacturing process mostly depended on manual production at present. Hence, the company planned to scale up its production capacity from 10,000 straws per day to 100,000 straws to meet the demand through automation or by increasing the production units to meet the growing demand from domestic and international markets. Kumarappan wondered if increasing the number of manufacturing facilities would make it harder to hire new staff, manage existing ones, train them and provide overall supervision; if these tasks were not completed well, the product’s quality and, subsequently, its demand, might suffer. The automation process required huge investment, time and a great deal of skepticism for its success. Kumarappan was stuck over whether to add more production units or automate the process to increase production.

This case study is suitable for graduate students.

Teaching notes are available for educators only.

CSS 3: Entrepreneurship.

Considering both the current opportunities of the Internet of things (IoT) and aviation, as well as the potential opportunities they may offer for the future, it is understood that they are among the important issues that need to be examined in the literature. This study aims to provide an idea by conducting bibliometric and visualization analyses of the current trends and development opportunities of IoT and aviation.

In this study, descriptive and bibliometric analyses within the framework of co-author, co-citation, bibliographic coupling, and keyword co-occurrence analysis were carried out for publications found to be published between 2007 and 2023 in the Web of Science (WoS) database related to IoT and aviation. VOSviewer (ver. 1.6.18) program and the Biblioshiny application were used to create bibliometric networks and provide visualization.

As a result of some descriptive and visualization analyses, the current trend of publications on IoT and aviation and future publication opportunities has been revealed. It has been understood that the subject of IoT and aviation is one of the subjects whose number of publications has increased in recent years and has not yet fully matured in terms of the number of publications and has the potential to make new publications.

In this study, bibliometric analysis of IoT and aviation, which could not be found examined before in the literature, and the creation of existing bibliometric networks by visualizing were carried out.

The paper uses foundation models to integrate the green approach in Logistics 5.0. Such integration is innovative in logistics and leads to a more sustainable and prosperous future. By harnessing the power of foundation models and incorporating sustainable principles, this paper can systematize the logistics industry’s environmental framework, increase its social responsibility and ensure its long-term economic viability.

Generalizing environmental sustainability goals requires a multi-layered innovation approach incorporating corporate philosophy, products, processes and business models. In this paper, this comprehensive approach is not just a strategy but a necessity in the current global context. This paper uses the sustainability-oriented innovation (SOI) method, crucial for achieving explicit environmental, social and economic impacts.

Artificial intelligence, especially foundation models, can contribute to green logistics by optimizing routes, reducing packaging waste, improving warehouse layouts and other functions presented in the paper. At the same time, they can also consider social, economic and governance goals.

Artificial intelligence algorithms present challenges such as high initial investment, regulatory compliance and technological integration.

The paper contains implications for developing environmentally sustainable logistics, which is currently one of the most significant challenges. The framework presented can apply to logistics companies.

This paper fulfills an identified need to study sustainability in logistics. The framework is entirely original and not present in the literature. It is essential to help design and implement innovative logistics approaches.

The integration of big data with artificial intelligence in the field of digital health has brought a new dimension to healthcare service delivery. AI technologies that provide value by using big data obtained in the provision of health services are being added to each passing day. There are also some problems related to the use of AI technologies in health service delivery. In this respect, it is aimed to understand the use of digital health, AI and big data technologies in healthcare services and to analyze the developments and trends in the sector.

In this research, 191 studies published between 2016 and 2023 on digital health, AI and its sub-branches and big data were analyzed using VOSviewer and Rstudio Bibliometrix programs for bibliometric analysis. We summarized the type, year, countries, journals and categories of publications; matched the most cited publications and authors; explored scientific collaborative relationships between authors and determined the evolution of research over the years through keyword analysis and factor analysis of publications. The content of the publications is briefly summarized.

The data obtained showed that significant progress has been made in studies on the use of AI technologies and big data in the field of health, but research in the field is still ongoing and has not yet reached saturation.

Although the bibliometric analysis study conducted has comprehensively covered the literature, a single database has been utilized and limited to some keywords in order to reach the most appropriate publications on the subject.

The analysis has addressed important issues regarding the use of developing digital technologies in health services and is thought to form a basis for future researchers.

In today’s world, where significant developments are taking place in the field of health, it is necessary to closely follow the development of digital technologies in the health sector and analyze the current situation in order to guide both stakeholders and those who will work in this field.

This study explores the effects of gender and participation in agricultural cooperatives on biodiversity conservation farming practices in vegetable production.

This study used data collected from a survey of 627 vegetable farmers in Viet Nam and employed the Ordered Probit regression model to examine the effects of gender and participation in agricultural cooperatives on biodiversity conservation farming practices.

We find that female vegetable farmers are more likely to conduct biodiversity conservation farming practices than male farmers. This gender difference is, however, removed when participation in agricultural cooperatives is controlled, suggesting that agricultural cooperatives effectively facilitate biodiversity conservation farming practices.

It is noted that our study is not free from some limitations. First, we conducted our study on vegetable farmers only. The biodiversity conservation practices in vegetable cultivation might be different from other types of farming. Future studies should be conducted with other types of agricultural cultivation. Second, we do not have enough data to explain why female farmers are more likely to adopt biodiversity conservation practices than male farmers. Future studies should capture biological and social aspects of gender differences to address this limitation.

This study contributes to the literature on biodiversity conservation by presenting empirical evidence on the effects of gender and agricultural cooperatives. Participation in agricultural cooperatives is revealed to facilitate the adoption of biodiversity conservation practices. In addition, we find that the education of farmers, the number of years that farmers have been living in the local area and the quality of land and water are positively related to the adoption of biodiversity conservation practices in vegetable production.

This study aims to investigate the impact of facial-based perceived trustworthiness on stock valuation, particularly, in the initial public offering (IPO). IPO settings provide the opportunity to investigate whether information asymmetry resulting from company newness in the market would influence the incorporation of soft information in the form of executive facial trustworthiness in stock valuation.

We use a recent machine learning algorithm to detect facial landmarks and then calculate a composite facial trustworthiness measure using several facial features that have previously been observed in neuroscience and psychological studies to be the most determining factor of perceived trustworthiness. We then regress the facial trustworthiness of IPO firm executives to IPO underpricing.

Utilizing machine learning algorithms, we find that the facial trustworthiness of the company executive negatively impacts the extent of IPO underpricing. This result implies that investors incorporate the facial trustworthiness of company executives into stock valuation. The IPO underpricing also shows that the cost of equity is higher when perceived trustworthiness is low. With regard to the higher information asymmetry in IPO transactions, such a negative impact implies the role of facial trustworthiness in alleviating information asymmetry.

This study provides evidence of the impact of top management personal characteristics on firms’ financial transactions in the Indonesian context. From the perspective of investors and other fund providers, this study shows evidence that heuristics still play an important role in financial decision-making. This is also an indication of investor reliance on soft information. Our research method also provides a new opportunity for the use of machine-learning algorithms in processing non-conventional types of data in finance research, which is still relatively rare in emerging markets like Indonesia. To the best of our knowledge, our study is the first to use personalized measures of trust generated through machine-learning algorithms in IPO settings in Indonesia.