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Small and medium enterprises (SMEs) play a crucial role in national economies worldwide, generating employment and contributing to innovation. This study tries to investigate the performance of the newly started IPO platform for the SMEs in India through a two-staged framework developed to measure pre-market and post-market underpricing separately and the impact of economic policy uncertainty (EPU) on the IPO returns using the EPU index which is based on newspaper coverage frequency. Further, the long-run performance of SME IPOs and the factors affecting the same have also been analyzed. The two-staged framework is helpful in capturing the impact of different factors separately on the two distinctive markets and providing effective investment strategies to the investors.

A sample of 384 SME IPOs issued during 2012–2018 has been analyzed using robust regression analysis.

The study highlights the fact that there are differences in the factors affecting pre-market and post-market underpricing and reports that investors subscription rate, issue expenses, lead manager reputation and EPU are positively associated, whereas the age of the firm is negatively associated with the pre-market underpricing, and lead manager reputation positively impacts the post-market underpricing whereas issue premium and pre-market underpricing are negatively associated. Pre-market underpricing subsumes all the impact of EPU (publicly available information) in it, hence providing credence to the semi-strong market hypothesis of the Efficient Market Hypothesis (EMH). The long-run performance of SME IPOs increases with time, and lead manager reputation, pre-market and post-market underpricing are positively related to the one-year return whereas issue size, turnover and issue expense are negatively related.

This paper is believed to be the first attempt to analyze the performance of SME IPOs by disaggregating IPO underpricing. The findings of this study will have a great insight for the investors and policymakers.

The current work explores the relationship between workplace ostracism, emotional intelligence (EI) and organizational change cynicism. This paper also aims to examine how dimensions of EI moderate the relationship between workplace ostracism and organizational change cynicism.

This study intends to examine the contribution of independent variable as well as moderating variable towards the outcome. Therefore, Pearson product–moment correlation and moderated regression analyses have been carried out to verify the hypotheses. To validate the tools upon the employees of India, authors have carried out measurement model analyses through AMOS and checked their composite reliability, convergent validity (average variance extraction [AVE]) and discriminant validity (square root of AVE). This study followed a simple random sampling technique with 276 employees (Male: N = 150, Mage = 34.34, female: N = 126, Mage = 31.57) from three manufacturing units of Odisha, an Eastern part of India.

Results showed that workplace ostracism was positively related to organizational change cynicism. However, only two dimensions of EI, namely, appraisal and regulation of self-emotion (ARSE) and other’s emotional appraisal were negatively related to organizational change cynicism. Moderated regression analysis indicates that positive relationship between workplace ostracism and organizational change cynicism is stronger for employees with low ARSE and other emotional appraisals than those with higher scores.

The researchers conclude this paper with inputs for developing a suitable training module on EI, specifically focusing on various emotional management skills.

To the best of the authors’ knowledge, this study is first of its kind on workplace ostracism and organizational change cynicism among Indian employees in the manufacturing sector. This study also examines the moderating role of EI on workplace ostracism and organizational change cynicism, which has been somehow inadequate in the present epoch.

The present research identifies a total of nine factors influencing tax avoidance under the international taxation regime of the developing countries and establishes a hierarchical relationship through modeling of the identified factors using modified-total interpretive structural modeling (M-TISM).

Due to “scale without mass” properties of the digital economy, businesses reduce their physical presence in the countries of economic activities. Aided with digital features, multinational enterprises (MNEs) avoid, abolish, or adopt flexible tax burden in the developing nations through by-passing the permanent establishment condition for company taxes or the income characterization prerequisite for royalty taxation. The present research endeavors to identify the drivers of tax avoidance in the developing countries, especially exacerbated due to digital technologies (economy). In addition, the authors also examine the hierarchical relation between the extracted drivers of tax avoidance.

This research presents a considerable driving force of elements like historical foundation of tax-treaties, dominance of the developed countries, influence of trade bodies in policy matters and finally information and communications technologies (ICTs).

Identified elements drive the actors like professional enablers, tax havens, international organizations, and intangible assets in the form of intellectual properties (IPs) which act upon tax arbitrage situations both under the domestic and treaty regulations, finally culminating into profit shifting, tax manipulations or avoidance.

Substantial pressure from civil society and investors has forced governments around the world to take climate neutrality initiatives. Several countries have pledged their nationally determined contributions towards net-zero. However, there exist various obstacles to achieving the same and the agriculture sector is one of them. Thus, this study identifies and models the critical barriers to achieving climate neutrality in the agriculture food supply chain (AFSC).

Sixteen barriers are identified through a literature survey and are validated by the questionnaire survey. Furthermore, the interactions amongst the barriers are estimated through the application of the “weighted influence non-linear gauge system (WINGS)” method which considers the both intensity of influence and the strength of the barrier. To mitigate these barriers, a framework based on green, resilient and inclusive development (GRID) is proposed.

The obtained results reveal that lack of collaboration amongst AFSC stakeholders, lack of information and education awareness, and lack of technical expertise obtained a higher rank (amongst the top five) in three indicators of the WINGS method and thus are the most significant barriers.

This paper is the first attempt in modelling the climate neutrality barriers for the Indian AFSC. Additionally, the mitigating strategies are prepared using the GRID framework.

Notable energy losses and voltage deviation issues in low-voltage radial distribution systems are a major concern for power planners and utility companies because of the integration of electric vehicles (EVs). Electric vehicle charging stations (EVCSs) are the key components in the network where the EVs are equipped to energize their battery. The purpose of this paper is coordinating the EVCS and distributed generation (DG) so as to place them optimally using swarm-based elephant herding optimization techniques by considering energy losses, voltage sensitivity and branch current as key indices. The placement and sizing of the EVCS and DG were found in steps.

The IEEE 33-bus test feeder and 52-bus Indian practical radial networks were used as the test system for the network characteristic analysis. To enhance the system performance, the radial network is divided into zones for the placement of charging stations and dispersed generation units. Balanced coordination is discussed with three defined situations for the EVCS and DG.

The proposed analysis shows that DG collaboration with EVCS with suitable size and location in the network improves the performance in terms of stability and losses.

Stability and loss indices are handled with equal weight factor to find the best solution.

The proposed method is coordinating EVCS and DG in the existing system; the EV integration in the low-voltage side can be incorporated suitably. So, it has societal impact.

In this study, the proposed method shows improved results in terms EVCS and DG integration in the system with minimum losses and voltage sensitivity. The results have been compared with another population-based particle swarm optimization method (PSO). There is an improvement of 18% in terms of total power losses and 9% better result in minimum node voltage as compared to the PSO technique. Also, there is an enhancement of 33% in the defined voltage stability index which shows the proficiency of the proposed analysis.

Jamun is an underutilised fruit crop of India whose utilization needs to be considerably increased. Despite its impressive nutritional profile and several health benefits, its cultivation is still very limited. Hence, this study aims to highlight the essential nutrients, health benefits and value-added products of jamun fruit and seed.

Major sources of bibliometric information such as Web of Science, Scopus, PubMed and Google Scholar were extensively searched with keywords such as nutritional composition of jamun, bioactive compounds, health benefits and jamun-based food products to obtain a database of 317 papers. Thirty four publications met the criteria for review.

Jamun fruit (Syzygium cumini L.) is known for its attractive colour, astringent taste, enormous nutrients and nutraceutical properties; nevertheless, this fruit is seasonal, perishable and underutilised. Furthermore, the fruit is used for the treatment of diabetes and also possess anti-cancer, anti-inflammatory, anti-obesity and cardio-protection properties. Investigations were done on the production of value-added food products such as read-to-serve beverages, jam, cookies and cake from jamun due to their several health benefits. The present review was an attempt to provide spotlights on the health benefits and food product applications of jamun fruit and its by-products.

This review systematically collates evidence from various published sources regarding the nutritional profile, development of food products from jamun and their health benefits. The compiled information will help researchers and professional become aware of the significance of jamun fruit and thereby enhances its processing and applications in food systems.

The study seeks to explore why despite growing concern for the environment, consumers’ intention to purchase organic foods remains relatively low. In addition, the study also seeks to investigate the role of perceived marketplace influence (PMI) and moral norms in organic food consumption.

Data collected from 330 young consumers chosen with non-probability sampling were analysed using structural equation modelling in Amos 22.0.

The results of the parallel mediation analysis confirmed that environmental concern influences purchase intention indirectly through attitude, subjective norms, perceived behavioural control and perceived marketplace influence. In addition, moral norms were found to moderate the effect of perceived behavioural control on purchase intention. Moreover, the results also indicated that the impact of environmental concern on consumers’ attitude toward organic foods was also moderated by moral norms. Further, the results of moderated mediation showed that the indirect effect of environmental concern on purchase intention (through attitude and perceived behavioural control) was moderated by moral norms.

The study contributes to the existing literature by investigating the inconsistency between environmental concern and purchase intention. In addition, the study also investigate role of perceived marketplace influence and moral norms in stimulating organic food consumption intentions.

The emergence of perceived marketplace influence as an important determinant of organic food consumption shows that every individual needs to realise the importance of their environment friendly actions to promote organic food consumption. In addition, the study also highlights the pivotal role of moral norms in the promotion of organic food consumption. Thus, markets, policy-makers, family, friends, society all should promote and inculcate the spirit of contributing in the cause of safeguarding the environment to the young children specially by promoting consumption of organic foods.

The study examines the role of perceived marketplace influence as predictor of purchase intention towards organic foods which is rarely explored specially in the domain of organic food consumption. In addition, the results also produced some novel insights into the moderating role of moral norms.

The study unravels the effects of climate-induced variations in staple crop yields on various migratory inflows in India while adjusting for seasonal weather and sociodemographic factors.

The instrumental variable approach is used to assess the potential effects of climate and nonclimate parameters on various migration types, exploiting panel data at the district level from the 2001 and 2011 Census years, with agriculture acting as the mediator.

As weather-driven variations in rice and wheat yield increase by 10%, the share of migration within and between districts to population decreases by 0.017 and 0.002, respectively. However, rice and wheat yields increase by 494.60 and 524.40%, respectively, with a marginal increase in the share of migration within states to population. Also, the elasticities of disadvantaged groups, literate locals and agricultural workers vary for different relocations.

The current study affirms climate migration through the agricultural channel at a finer spatial scale, asserting the sensitivity aspect of disparate movements to periodic weather and heterogeneous clusters. This is critical for effectively implementing targeted public policies in the face of increasing climate risks.

The peer review history for this article is available at https://publons.com/publon/10.1108/IJSE-10-2022-0710

Today’s in-demand skills may not be needed tomorrow. As companies are adopting a new group of technologies, they are in huge need of information technology (IT) professionals who can fill various IT positions with a mixture of technical and problem-solving skills. This study aims to adopt a sematic analysis approach to explore how the US Information Systems (IS) programs meet the challenges of emerging IT topics.

This study considers the application of a hybrid semantic analysis approach to the analysis of IS higher education programs in the USA. It proposes a semantic analysis framework and a semantic analysis algorithm to analyze and evaluate the context of the IS programs. To be more specific, the study uses digital transformation as a case study to examine the readiness of the IS programs in the USA to meet the challenges of digital transformation. First, this study developed a knowledge pool of 15 principles and 98 keywords from an extensive literature review on digital transformation. Second, this study collects 4,093 IS courses from 315 IS programs in the USA and 493,216 scientific publication records from the Web of Science Core Collection.

Using the knowledge pool and two collected data sets, the semantic analysis algorithm was implemented to compute a semantic similarity score (DxScore) between an IS course’s context and digital transformation. To present the credibility of the research results of this paper, the state ranking using the similarity scores and the state employment ranking were compared. The research results can be used by IS educators in the future in the process of updating the IS curricula. Regarding IT professionals in the industry, the results can provide insights into the training of their current/future employees.

This study explores the status of the IS programs in the USA by proposing a semantic analysis framework, using digital transformation as a case study to illustrate the application of the proposed semantic analysis framework, and developing a knowledge pool, a corpus and a course information collection.

Investors who can transfer their savings to investments in a well-regulated market benefit not only themselves but also economic development. Hence, it is crucial for fund owners to evaluate their stock market investment decisions. The goal of the study is to understand which model determines the asset returns most efficiently. In this regard, the validity of single and multi-index asset pricing models (capital asset pricing model-CAPM and Fama–French models) has been examined in the Turkish Stock Exchange for 2009–2020, with the quantile regression (QR) approach.

On 18 portfolios comprised of quoted stocks in the Istanbul Stock Exchange 100 (ISE-100/BIST-100), we test the CAPM, the Fama and French three factor model (FF3) and the Fama and French five factor model (FF5). Empirical analyses have been carried out via QR approach regressing the portfolios' average weekly excess returns on risk premium/market factor (Rm-Rf), firm size, book value/market value (B/M), profitability and investments factors. QR estimation has been employed since QR is more effective and provides a better definition of the distribution’s tails.

Our empirical findings have revealed that the average excess weekly returns can be explained more strongly via CAPM. Moreover, Fama and French models are expected to give more reliable result with more data, whereas the market premium would give robust results for the Turkish Capital Market.

Individuals investing in financial assets must find the price model that best fits the market. The return can be approximated in the most appropriate manner using the right variables.

The study differs from other research by comparing the asset pricing models via examining the assets' weekly returns with QR in the Istanbul Stock Exchange 100 (ISE-100).