Search results

This study aims to examine the reaction of anchor investors (AIs) to pre-IPO earnings management (EM). The authors use the unique detailed bid data from the Indian anchor experiment. The authors also study the reputed AIs’ EM detection ability and pricing behavior in response to pre-IPO EM.

The authors use unique AI bid data for 169 Indian IPO firms. Utilizing the logistic regression and Tobit regression models with industry and year-fixed effects, the authors examine the relationship between various measures of AI participation and proxies of short-term and long-term discretionary accruals.

The authors document that pre-IPO EM is positively associated with the likelihood of anchor backing but negatively related to the likelihood of reputed anchor backing. The findings indicate that AIs are misled by pre-IPO EM, but reputed AIs are not. The authors also observe that reputed AIs, compared to the non-reputed, pay less than the upper band with increasing EM. The findings are robust to using various AI measures and EM proxies.

The findings have significant implications for regulators in the implementation of AI concept in non-anchor markets and better implementation of policies in existing anchor settings. Findings can also be relevant for non-institutional investors in the IPO domain.

This is one of the few studies on institutional investors' IPO bidding behavior in response to pre-IPO EM. However, this is the first study to analyze AIs' IPO bidding behavior in response to pre-IPO EM.

This study aims to address the existing knowledge gap by investigating teachers’ adoption of ChatGPT for educational purposes. The study specifically focuses on identifying the factors that motivate and inhibit teachers in adoption of ChatGPT in higher education institutions (HEIs).

This research has used interpretative phenomenological analysis – a qualitative approach. Through in-depth interviews among the teachers, data was collected to identify the motivating and inhibiting factors that impacted teachers’ willingness to adopt ChatGPT. The data was collected from 48 teachers working across HEIs of Uttarakhand region in India.

The analysis revealed seven themes under motivating factors that encourage teachers to adopt ChatGPT for their educational purposes. These include time factor, tool for competitive edge, learning enhancement tool for students, research facilitator, benefits in educational settings, troubleshooter and easy to use. On the other hand, inhibiting factors comprise five themes, which include technical difficulties, limited features for educational and research purposes, tool for handicapping innovation and creativity, lack of personal touch and ethical considerations.

The findings will be valuable for HEIs in establishing policies that promote the appropriate and effective use of ChatGPT. Moreover, the study provides recommendations to ChatGPT solution providers for improving ChatGPT services for effective adoption of ChatGPT among teachers and implementation at HEIs. Further, it contributes to the body of literature by filling a knowledge gap about teacher adoption of ChatGPT in the HEIs. Through qualitative research, the study has pinpointed specific motivating and inhibiting factors that affect teacher adoption of ChatGPT.

Unlike previous studies that primarily explored the potential advantages and drawbacks of ChatGPT in education, this research study delves deeper into the topic. It makes a substantial contribution to our understanding of ChatGPT adoption among teachers by identifying distinct factors that either motivate or inhibit teachers from adopting ChatGPT for job related purposes. The study provides novel insights that were previously mislaid, thereby introducing a fresh perspective to the existing literature

The link between entrepreneurial intention and positive attitudes towards entrepreneurship for established and nascent entrepreneurs has been well documented in the extant literature, with the theory of planned behaviour (TPB) viewing entrepreneurial intention as a pre-requisite for entrepreneurial pursuit. Whilst scholars generally agree on these insights, little empirical evidence exists on how marginalised social groups can convert their intentions into action. This study aims to understand to what extent the elements of TPB, the attitudes towards entrepreneurship, self-efficacy and subjective norms, help explain the emergence of entrepreneurial activity amongst marginalised demographic groups.

This research focuses on unemployed women residing in social housing located in a deprived urban area of the United Kingdom to empirically examine how multiple layers of disadvantage faced by this group shape their motivations and intentions for entrepreneurial pursuit. A multi-source qualitative methodology was adopted, drawing upon inductive storytelling narratives and extensive fieldwork on a sample of unemployed ethnic minority women residing in social housing in a deprived urban area of the United Kingdom. Community organisation representatives and housing association employees within the social housing system were included to assess the interpretive capacity of TPB.

The findings display that TPB illuminates why and how marginalised groups engage in entrepreneurship. Critically, women’s entrepreneurial intentions emerge as a result of their experiences of multiple layers of disadvantage, their positionality and the specificity of few resources they can activate from their disadvantageous position for entrepreneurial activity.

By illuminating the linkages between marginalised women’s positionality and their associated access to the limited pool of resources using the TPB lens, this study contributes to emerging works on disadvantaged populations and entrepreneurial intention-action debate. This work posits that despite facing significant additional challenges through their positionality and reduced ability to mobilise resources, women in social housing can defy the odds and develop ways to overcome limited capacity and structural disadvantage.

The potential for massive economic growth exists in Samarinda City due to the intensification of activities in built-up areas. This suggests the potential for increased urban disease in the relocation of Indonesia’s new capital city to a location adjacent to Samarinda. One of the most striking impacts is the urban heat island (UHI). The increase in this phenomenon can be addressed effectively and efficiently through the provision and arrangement of appropriate vegetation-based actions. Therefore, this study aims to identify priority areas of green open space (GOS) based on UHI levels. In addition, this study also aims to present alternative mitigation measures to reduce the risk of disasters due to UHI.

A mixed-method approach was used in this research, involving an initial land surface temperature analysis to identify the UHI class. This analysis was complemented by quantitative spatial analyses, such as scoring, overlay and intersect methods, to determine the priority level class and the typology of GOS priority. A qualitative analysis was also conducted through data triangulation or comparison methods, such as examining existing land use, GOS priority maps and spatial plan policies.

The findings show that the total UHI area in Samarinda City was 6,936.4 ha in 2019 and is divided into three classifications. In Class 1, the UHI area is very dominant, reaching 87% of the total area. Meanwhile, the main results identified two priority classes of GOS in Samarinda, namely, the medium and high categories with an area of 960.43 ha and 113.57 ha, respectively. The results also showed that there were 17 typologies associated with five alternative mitigation measures: green industry, greening parking lots, improving urban green infrastructure and buildings, urban greening and mining restoration.

Specific to assessing UHI, image data were available only in medium spatial resolution, leading to a consequence of detailed accuracy. In addition, since the determination of mitigation considered local policies, the method should be used in other locations requiring adjustments to existing regulations, specifically those related to spatial planning.

This study makes a significant contribution to the understanding of the UHI phenomenon in Indonesia, especially in the urban areas of Kalimantan Island. In addition, the study presents new insights into alternative mitigation actions to reduce the risk of UHI. Innovatively, this study introduces a typology of regions associated with appropriate alternative mitigation actions, making it an important achievement for the first time in the context of this study.

This study aims to highlight the growth and development of Indo-German collaborative research over the past three decades. Moreover, this study encompasses an in-depth examination of funding acknowledgements to gain valuable insights into the financial support that underpins these collaborative endeavours. Together with this paper, the authors provide an openly accessible data set of Indo-German research papers for further and reproducible research activities (the “Indo-German Literature Dataset”).

The data were retrieved from the Web of Science (WoS) database from the year 1990 till the 30th of November 2022. A total of 36,999 records were retrieved against the used query. Acknowledged entities were extracted using a named entity recognition (NER) model specifically trained for this task. Interrelations between the extracted entities and scientific domains, lengths of acknowledgement texts, number of authors and affiliations, number of citations and gender of the first author, as well as collaboration patterns between Indian and German funders were examined.

The study reveals a consistent and increasing growth in the publication trend over the years. The study brings to light that Physics, Chemistry, Materials Science, Astronomy and Astrophysics and Engineering prominently dominate the Indo-German collaborative research. The USA, followed by England and France, are the most active collaborators in Indian and German research. Largely, research was funded by major German and Indian funding agencies, international corporations and German and American universities. Associations between the first author’s gender and acknowledged entity were observed. Additionally, relations between entity, entity type and scientific domain were discovered.

The study paves the way for enhanced collaboration, optimized resource utilization and societal advantages by offering a profound comprehension of the intricacies inherent in research partnerships between India and Germany. Implementation of the insights gleaned from this study holds the promise of cultivating a more resilient and influential collaborative research ecosystem between the two nations.

The study highlights a deeper understanding of the composition of the Indo-German collaborative research landscape of the past 30 years and its significance in advancing scientific knowledge and fostering international partnerships. Furthermore, the authors provide an open version of the original WoS data set. The Indo-German Literature Data set consists of 22,844 papers from OpenAlex and is available for related studies like literature studies and scientometrics.

The importance of service firms cooperating with digital platforms is widely acknowledged. The authors study three contractual relationships (fixed-cost, cost-sharing, and profit-sharing) between service firms (specifically hotels) and digital platforms in a highly fragmented service supply chain to examine which of these contract types optimizes profits.

The authors extend prior models analyzing the optimal expected total profit from the travel service firm (hotel)–digital platform relationship, providing new insights into each contract type’s ability to coordinate decentralized systems and optimize profits for both parties.

This study finds that fixed cost contracts cannot coordinate the decentralized system. Cost-sharing contracts can coordinate the decentralized system but only allow one channel profit split. In contrast, profit-sharing contracts may not always perfectly coordinate the decentralized system but support alternative profit allocations. Practically, both profit-sharing and cost-sharing contracts are preferable to fixed-cost contracts.

The paper includes implications for travel service firm managers to consider when structuring contracts with digital platforms to focus on profit optimization. Profit-sharing contracts are most preferable when cost and revenue data are fully shared between parties, while cost-sharing contracts are preferable over fixed-cost contracts.

This study extends prior investigations into the utility of different contract types on the optimal profit of a travel service firm (hotel)-digital platform provider relationship. The research fills a gap in the literature concerning the contracts used in these relationship types.

The context of sustainable development dwells, quite significantly, upon the one of gender parity in a society and nation. This is so because the issue of gender equality is key to distributive justice, which is in turn much essential for creating a good amount of precondition for sustainable development. Academic inquests into the problem into the gender disparity are indicative of how gender disparity on economic and social parameter triggers a negative productivity change over time and space. Thus, the current chapter brings forth an analytical approach to contemplating into the above-mentioned narratives in both theoretical and empirical terms. The tack of our analysis is as follows. To begin with, this chapter develops an index to determine the extent of gender disparity in health, education and participation in workforce (namely, Gender Gap Index or GGI). Moving on, the study extends to looking at India changing dynamics on gender gap vis-à-vis developing and less-developed countries. Besides, a general equilibrium model has been developed to unfurl the fallout of gender disparity in terms of the wage gap between male and female workers in the labour force, extant and conditioned further by demographic and sociopolitical factors.

This paper aims to examine whether managerial sentiment, extracted from annual reports, is associated with corporate risk-taking in the context of Chinese companies. This study expands the vein of literature on overconfidence theory.

By leveraging textual analysis on Chinese listed companies’ annual reports, the authors construct firm-level managerial sentiment during 2007 and 2021 to examine how managerial sentiment influences corporate risk-taking after control for firm characteristics. Corporate risk-taking is denoted by corporate investment engagements: capital expenditures and net fixed asset investment.

Results show that incentives for corporate risk-taking are likely to increase with the positive managerial sentiment and decrease with the negative sentiment in companies’ annual reports. Positive managerial sentiment is associated with over-/under-investment and low/high investment efficiency. Further additional tests show that the managerial sentiment effect only holds during low economic uncertain years and samples of private-owned firms. Furthermore, the robust tests indicate that there is no endogenous issue between managerial sentiment and corporate risk-taking.

Annual report textual-based managerial sentiment may not perfectly reflect managers’ lower frequency sentiment (e.g. weekly, monthly and quarterly sentiment). Future studies could attempt to capture managers’ on-time sentiment by using media sources and corporate disclosures.

To the best of the authors’ knowledge, this paper is the first research to provide insights into supervising managers’ corporate decisions by observing their textual information usage in corporate disclosure. Moreover, the approach of measuring managerial sentiment might be a solution to monitoring managerial class.

This paper contributes to the literature on accounting and finance studies, adding another piece of empirical evidence on content analysis by examining a unique language and institutional context (i.e. China). Besides, the paper notes that in line with the English version disclosure, based on Chinese semantic words, managerial sentiment in the Chinese-speaking world has magnitude on corporate decisions. The research provides insights into supervising managers’ corporate decisions by observing their textual information usage in corporate disclosure. Moreover, the approach to measuring managerial sentiment may be a practical solution to monitoring managerial class.

The proponents of cash waqf speak highly about its huge potential for mobilizing the third sector of the economy to fund the socio-economic development agenda. However, the under-collection issue has been characterizing the cash waqf movement globally. This study aims to examine how understanding the distinct cash waqf donating behavior across different generations has the potential to address the problem.

This study extends the theory of planned behavior by adding religiosity and knowledge variables into the standard model, using the partial least square structural equation modeling. A survey is conducted on 684 respondents representing the main provinces in Indonesia and four major generations (Baby Boomers [BB], Generations X, Y and Z).

Religiosity, Knowledge, Attitude, Subjective Norms and Perceived Behavioral Control directly or indirectly affect cash waqf intention. The effect is contingent on the characteristics of generations.

This study covers only the Indonesian case with limited coverage of the more heterogeneous provinces in the country. The sample distribution for BB can also be enlarged.

Cash waqf institutions (government and private) should apply the dynamic segmenting strategy, where the diversification of the promotion, marketing, awareness and approaches are contingent on the different characteristics of each generation.

To the best of the authors’ knowledge, this is the first study evaluating the intergenerational determinants of Intention toward cash waqf, particularly in Indonesia.

Every day, small and big incidents happen all over the world, and given the human, financial and spiritual damage they cause, proper planning should be sought to deal with them so they can be appropriately managed in times of crisis. This study aims to examine humanitarian supply chain models.

A new model is developed to pursue the necessary relations in an optimal way that will minimize human, financial and moral losses. In this developed model, in order to optimize the problem and minimize the amount of human and financial losses, the following subjects have been applied: magnitude of the areas in which an accident may occur as obtained by multiple attribute decision-making methods, the distances between relief centers, the number of available rescuers, the number of rescuers required and the risk level of each patient which is determined using previous data and machine learning (ML) algorithms.

For this purpose, a case study in the east of Tehran has been conducted. According to the results obtained from the algorithms, problem modeling and case study, the accuracy of the proposed model is evaluated very well.

Obtaining each injured person's priority using ML techniques and each area's importance or risk level, besides developing a bi-objective mathematical model and using multiple attribute decision-making methods, make this study unique among very few studies that concern ML in the humanitarian supply chain. Moreover, the findings validate the results and the model's functionality very well.