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The study examines the IPO resilience grounded on the firm’s intrinsic factors.

We examine the association of IPO performance and post-listing firm’s performance with issuers' pre-listing financial and qualitative traits using panel data regression.

IPOs floated in the Indian market from July 2009 to March 31, 2022, evince the notable influence of issuers' pre-IPO fundamentals and legitimacy traits on IPO returns and post-listing earning power. Where the pandemic’s favorable impact is discerned on the post-listing year earning power of the issuer firms, the loss-making issuers appear to be adversely affected by the Covid disruption. Perhaps, the successful listing equipped the issuers with the financial flexibility to combat market challenges vis-à-vis failed issuers deprived of desired IPO proceeds.

High initial returns followed by a declining pattern substantiate the retail investors to be less informed vis-à-vis initial investors, valuers and underwriters, who exit post-listing after profit booking. Investing in the shares of the newly listed ventures post-listing in the secondary market can shield retail investors from the uncertainty losses of being uninformed. The IPO market needs stringent regulations ensuring the verification of the listing valuation, the firm’s credentials and the intent of utilizing IPO proceeds. Healthy development of the IPO market merits reconsidering the listing of ventures with weak fundamentals suspected to withstand the market challenges.

Given the tremendous rise in the new firm venturing into the primary market and the spike in IPOs countering the losses immediately post-opening, the study examines the loss-making and young firms IPOs separately, adding novelty to the study.

This study aims to outline the research field of initial public offerings (IPOs) pricing and performance by combining bibliometric analysis with a systematic literature review process.

The study uses over three decades of IPO publication records (1989–2020) from Scopus and Web of Science databases. An analysis of keyword co-occurrence and bibliometric coupling was used to gain insights into the evolution of IPO literature.

The study categorized the IPO research field into four primary clusters: IPO pricing and short-run behaviour, IPO performance and influence of intermediaries, venture capital financing and top management and political affiliations and litigation risks. The results offer a framework for delineating research advancements at different stages of IPOs and illustrate the growing interest of researchers in IPOs in recent years. The study identified future research potential in the areas of corporate governance, earning management and investor sentiments related to IPO performance. Similarly, the study highlighted the opportunity to test multiple theoretical frameworks on alternative investment platforms (SME IPO platforms) operating under distinct regulatory environments.

To the best of the authors’ knowledge, this paper represents the first instance of using both bibliometric and systematic review to quantitatively and qualitatively review the articles published in the area of IPO pricing and performance from 1989 to 2020.

The purpose of this study was to develop a model of skilful surfing to aid understanding of how best to seek health information, online and offline, during pregnancy.

This study used an observational, descriptive design, using a purpose written questionnaire, factor analysis and structural equation modelling.

Analysis resulted in the factor loading of five components: online health information seeking behaviour, normalisation, offline support, trust and data saturation. These components were included as latent variables in an SEM to evaluate the credibility, and subsequently confirm the viability of the theory of skilful surfing.

To the authors' knowledge, this study is the first of its kind to successfully model and define what it is to skilfully surf health information online whilst pregnant, with empirical and theoretical underpinnings.

This study aims to identify how the involvement of female university students in volunteer organisations is contributing to their self-empowerment in Bangladesh.

A cross-sectional study with a qualitative approach was conducted. A total of 20 in-depth interviews with female student volunteers, two focus group discussions with student members of the volunteer organisation and ten key informant interviews with academics, gender experts and high-level representatives of civil society organisations were conducted. The data were collected from eight purposively selected universities in Dhaka. The data were collected between 1 January 2023 and 23 March 2023.

The inter-organisational environment appears to be supportive and welcoming, despite the fact that some male colleagues belittle women with insulting comments. Female students are predominantly involved in public presentation activities and their advice is rarely considered. To reach a top position, they have to show more commitment than their male colleagues. Subjective prejudices discourage women from taking on positions of responsibility. Participation in voluntary organisations strengthens the self-confidence, critical thinking, networking and social capital of female students, making it easier for them to gain employment. These factors contribute to the empowerment of female students.

This study is an excellent empirical document that establishes a link between the empowerment of female students and their participation in voluntary activities. The recommendations of this study pave the way for future research in different countries on how volunteering helps young people, especially female students, to shape their future by taking on social responsibility alongside their academic activities.

This survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these challenges, providing insights into healthcare investments, policy analysis and patient care pathways.

This research employs the real options theory, a financial concept, to delve into health economics challenges. Through a systematic approach, three distinct models rooted in this theory are crafted and analyzed. Firstly, the study examines the value of investing in emerging health technology, factoring in future advantages, associated costs and unpredictability. The second model is patient-centric, evaluating the choice between immediate treatment switch and waiting for more clarity, while also weighing the associated risks. Lastly, the research assesses pandemic-related government policies, emphasizing the importance of delaying decisions in the face of uncertainties, thereby promoting data-driven policymaking.

Three different real options models are presented in this study to illustrate their applicability and value in aiding decision-makers. (1) The first evaluates investments in new technology, analyzing future benefits, discount rates and benefit volatility to determine investment value. (2) In the second model, a patient has the option of switching treatments now or waiting for more information before optimally switching treatments. However, waiting has its risks, such as disease progression. By modeling the potential benefits and risks of both options, and factoring in the time value, this model aids doctors and patients in making informed decisions based on a quantified assessment of potential outcomes. (3) The third model concerns pandemic policy: governments can end or prolong lockdowns. While awaiting more data on the virus might lead to economic and societal strain, the model emphasizes the economic value of deferring decisions under uncertainty.

This research provides a quantified perspective on various decisions in healthcare, from investments in new technology to treatment choices for patients to government decisions regarding pandemics. By applying real options theory, stakeholders can make more evidence-driven decisions.

Decisions about patient care pathways and pandemic policies have direct societal implications. For instance, choices regarding the prolongation or ending of lockdowns can lead to economic and societal strain.

The originality of this study lies in its application of real options theory, a concept from finance, to the realm of health economics, offering novel insights and analytical tools for decision-makers in the healthcare sector.

The rapid global use of Financial Technology (FinTech), an innovative and advanced technology, is leading to the replacement of traditional financial services. Despite this, consumers in the United Arab Emirates are still not accepting FinTech services as anticipated. This study aims to investigate the barriers preventing consumers from using FinTech services in the banking industry based on the technology-organisation-environment (TOE) framework and anchored by the unified theory of acceptance and use of technology (UTAUT).

The current study used an exploratory sequential mixed-method design. A purposive sample was used to conduct preliminary semi-structured interviews with ten banking practitioners. These interviews aimed to explore the obstacles that hinder consumers from adopting FinTech services. To provide further empirical validation, the conceptual model has been systematically examined through a quantitative cross-sectional online survey encompassing a sample of 332 bank customers.

The qualitative investigation highlighted six new barriers to the usage of FinTech based on TOE’s model attributes. Through quantitative data analysis, the preliminary qualitative findings were primarily verified. It was discovered that self-efficacy, electronic word-of-mouth, system quality and bank image substantially influence consumers to use FinTech services, in addition to performance expectancy and facilitating conditions. Meanwhile, IT features and governmental support were found to have insignificant effects. Yet, government support was found to have a positive moderating role in strengthening social influence and system quality towards customer use of FinTech. Thus, the research supports the UTAUT and TOE models’ fitness for explaining FinTech usage with high predictive relevance.

This study is the authors’ pioneering effort to propose a comprehensive research framework that integrates the UTAUT and TOE models to tackle the limited adoption of FinTech services among consumers. Moreover, the study specifically examined the moderating influence of government support to propose an integrated model. Remarkably, this study is one of the initial attempts to investigate the adoption of FinTech using a mixed-methodology approach by incorporating quantitative and qualitative methods within a single inquiry to yield more comprehensive and insightful findings than research that relies on a single method.

A wide gap exists between the innovation and development of self-monitoring, analysis and reporting technology (SMART) technologies and the actual adoption by older adults or those caring for them. This paper aims to increase awareness of available technologies and describes their suitability for older adults with different needs. SMART technologies are intelligent devices and systems that enable autonomous monitoring of their status, data analysis or direct feedback provision.

This is a scoping review of SMART technologies used and marketed to older adults or for providing care.

Five categories of SMART technologies were identified: (1) wearable technologies and smart tools of daily living; (2) noninvasive/unobtrusive technology (i.e. passive technologies monitoring the environment, health and behavior); (3) complex SMART systems; (4) interactive technologies; (5) assistive and rehabilitation devices. Technologies were then linked with needs related to everyday practical tasks (mainly applications supporting autonomous, independent living), social and emotional support, health monitoring/managing and compensatory assistance rehabilitation.

When developing, testing or implementing technologies for older adults, researchers should clearly identify concrete needs these technologies help meet to underscore their usefulness.

Older adults and caregivers should weigh the pros and cons of different technologies and consider the key needs of older adults before investing in any tech solution.

SMART technologies meeting older adult needs help support both independent, autonomous life for as long as possible as well as aiding in the transition to assisted or institutionalized care.

This is the first review to explicitly link existing SMART technologies with the concrete needs of older adults, serving as a useful guide for both older adults and caregivers in terms of available technology solutions.

The present study reviews the literature on the history and evolution of investment strategies in the stock market for the period from 1900 to 2022. Conflicts and relationships arising from such diverse seminal studies have been identified to address the research gaps.

The studies for this review were identified and screened from electronic databases to compile a comprehensive list of 200 relevant studies for inclusion in this review and summarized for the cognizance of researchers.

The study finds a coherence to complex theoretical documentation of more than a century of evolution on investment strategy in stock markets, capturing the characteristics of time with a chronological study of events.

There were complications in locating unpublished studies leading to biases like publication bias, the reluctance of editors to publish studies, which do not reveal statistically significant differences, and English language bias.

Practitioners can refine investment strategies by incorporating behavioral finance insights and recognizing the influence of psychological biases. Strategies span value, growth, contrarian, or momentum indicators. Mitigating overconfidence bias supports effective risk management. Social media sentiment analysis facilitates real-time decision-making. Adapting to evolving market liquidity curbs volatility risks. Identifying biases guides investor education initiatives.

This paper is an original attempt to pictorially depict the seminal works in stock market investment strategies of more than a hundred years.

This paper focuses on image-to-text manuscript processing through Handwritten Text Recognition (HTR), a Machine Learning (ML) approach enabled by Artificial Intelligence (AI). With HTR now achieving high levels of accuracy, we consider its potential impact on our near-future information environment and knowledge of the past.

In undertaking a more constructivist analysis, we identified gaps in the current literature through a Grounded Theory Method (GTM). This guided an iterative process of concept mapping through writing sprints in workshop settings. We identified, explored and confirmed themes through group discussion and a further interrogation of relevant literature, until reaching saturation.

Catalogued as part of our GTM, 120 published texts underpin this paper. We found that HTR facilitates accurate transcription and dataset cleaning, while facilitating access to a variety of historical material. HTR contributes to a virtuous cycle of dataset production and can inform the development of online cataloguing. However, current limitations include dependency on digitisation pipelines, potential archival history omission and entrenchment of bias. We also cite near-future HTR considerations. These include encouraging open access, integrating advanced AI processes and metadata extraction; legal and moral issues surrounding copyright and data ethics; crediting individuals’ transcription contributions and HTR’s environmental costs.

Our research produces a set of best practice recommendations for researchers, data providers and memory institutions, surrounding HTR use. This forms an initial, though not comprehensive, blueprint for directing future HTR research. In pursuing this, the narrative that HTR’s speed and efficiency will simply transform scholarship in archives is deconstructed.