Search results

This study aims to examine the impact of signaling through social media (SM) on funding achieved by start-ups.

This study follows a causal research design and is based on unique data set compiled from Crunchbase-Pro and Twitter. The sample size is 1,672 Indian start-ups. Heckman’s model and ordinary least squares regression is used to test the hypothesis.

Devising a thoughtful SM strategy, should be an integral part of the overall strategy of the start-ups looking out for funds. LinkedIn presence is in itself a positive signal. Active usage of Twitter and feedback from other Twitter users has a positive impact on funds raised by the start-up. Posting retweets and repetitive usage of URLs and media is not a predictor of funds raised by the start-up.

An early-stage strategy on SM adoption, especially Twitter can play an important role in attracting interest and attention of stakeholders. To capitalize SM, entrepreneurs should maintain an active SM account of the start-up.

India has emerged as one of the start-up hubs of the world. However, there is a dearth of literature on SM usage by start-ups in India. To the best of the authors’ knowledge, this study is first of its kind and establishes the results empirically based on more than 100k tweets for a large pool of Indian start-ups.

This paper empirically investigates the demand for pastured eggs in the United States and evaluates the welfare consequences of Japan's egg import tariff reductions for the US consumers.

Using household-level Nielsen Homescan panel data, a fixed-effects Heckman two-stage sample selection model is estimated.

The estimation results ascertain the importance of a set of household socioeconomic characteristics, which are found to influence both the purchase probabilities and the consumption amounts associated with pastured eggs. In addition, demand for pastured eggs is estimated to be inelastic, and pastured eggs are found to be a normal good, more specifically a luxury.

The dataset used in this study reflect purchases only for at-home consumption, lacking information on away-from-home purchases.

Building upon previous research, this study makes the following distinct contributions to the current literature. To the best of our knowledge, it constitutes the first study to empirically examine the demand for pastured eggs, using household-level panel data and an estimation model that not only allows for left-censoring but also controls for regional and time fixed effects. Second, the present study reflects a unique effort in analyzing the adverse welfare consequences of the increased egg prices in the United States brought by a reduction of Japanese import tariffs on US-supplied eggs, focusing specifically on pastured eggs.

This study examines the relationship between investors' familiarity bias, including the home bias and endowment bias, and their financial situations, expectations and personal characteristics.

Using the 2019 Survey of Consumer Finances, the authors utilize an ordinary least squares regression to identify the presence of endowment bias and home bias in individual investors' direct stock holdings and use a Heckman selection model to examine determinants of the extent of endowment bias and home bias.

This study finds that investors with higher income and more education, men, non-white investors and people with greater risk tolerance are actually at a greater risk of endowment bias. This study also identifies a profile of investors that are more likely to have a home bias: with less financial sophistication, lower net worth, older, female, more risk-averse, with a positive expectation about the domestic economy and a relatively shorter investment horizon.

This paper is among the first to use US investors' directly reported stock holdings to examine the individual characteristics that are correlated with greater familiarity bias, providing financial professionals with information about how to allocate their limited time in providing education to a variety of clients.

Participation of women in engineering education is considerably low in India, although it is increasing in recent years. Also, engineering is primarily treated as a male-dominated profession, and the authors do not find many women in this sector. What factors contribute to this significant gender differences in engineering education and labour market in India? In this context, this study aims to examine the factors that explain the gender variations in academic performance and labour market outcomes (placement and earnings) of engineering graduates in India.

The paper is based on primary survey data from fourth-year engineering students in Delhi, collected in 2018–2019, with a total sample size of 3186. The study uses Ordinary least square method (OLS) and Heckman selection model to analyse gender differences in academic performance and labour market outcomes of engineering graduates, respectively.

The study finds that academic performance of male students is around 10.4% more than female students. However, this difference is heavily influenced by various socioeconomic and institutional factors. Interestingly, 3% of female engineering graduates have received more job offers than males, which contradicts the common belief that women engineers face job discrimination in the labour market in India. However, the authors find that male engineers earn around 7% more than female engineers shows the evidence of pro-male gender wage inequality in earnings. The findings support that there is a considerable variation in academic performance and earnings between male and female engineering graduates.

While the authors find some literature in the area of gender difference in the academic performance and labour market among university graduates in India, studies in the field of engineering education are sparse. In a context where fewer women are found in the field of engineering education along with low participation in the labour market, the findings of this study significantly contribute to the policy making.

The paper aims to examine the impact of Shari'ah governance quality on environmental, social and governance (ESG) performance in Islamic banks.

The study's sample consists of 66 Islamic banks from 14 countries over 2015–2019. The research uses the Heckman model, which is a two-stage estimation method to obtain unbiased estimates, as ESG scores are only observable for 17 Islamic banks in Eikon Refinitiv database at the time of the analysis.

The analysis shows that Shari'ah governance has a beneficial role to achieve ESG performance. The analysis also shows that enhanced profiles of Shari'ah supervisory boards' (SSB) attributes are more efficient than the operational procedures to promote ESG performance. In addition, the analysis shows that enhanced SSBs' attributes strengthen the bank's corporate governance framework, while sound-designed procedures increase the bank's social activities by emphasizing their roles to ensure Shari'ah compliance. Finally, the analysis sheds light on the failure of Shari'ah governance to promote environmental performance.

The existing databases providing companies' ESG-related information still do not offer sufficient data to conduct an international study with a larger sample of Islamic banks (IBs) having ESG scores for a more extended period.

The research provides policy insights to Islamic banks' stakeholders to promote social and governance performance in the Islamic finance industry through improving Shari'ah governance practices. However, raising environmental awareness is imminent among all actors implicated in the Shari'ah governance processes to help overcome the anthropogenic risks.

The research complements the governance-banks' ESG performance literature by examining the role of Shari'ah governance. The research also extends the literature on Islamic banks' sustainability by pointing to the Shari'ah governance failure to enhance environmental performance and thus achieve Maqasid al-Shariah regarding the environment.

This study aims to identify extensive and intensive margins in exports and imports and examine whether incoming foreign direct investments (FDI) benefit local firms in Indonesia through the export and import channels.

Using Heckman’s two-step selection model to consider the potential of bias of self-selection in export–import participation, this study uses the firm-level data from 2008 to 2015 collected from Statistik Industri and proximate both export and import spillovers.

The authors found that internal factors are critical for a firm to be an exporter, signaling self-selection in exports and imports. Spillover effects from FDI (spatial properties) support export but lower import propensity and intensity.

This study implies that improving human capital (absorptive capacity) is needed to accelerate export intensity and policies supporting FDI inflows in complementary sectors (noncompeting industries) can increase export propensity and intensity and reduce imports.

This study contributes to the literature in several ways. First, the proposed export spillovers model that accounts for impacts through a demonstration channel is applied to the import channel. Moreover, this study extends the model developed by Franco and Sasidharan (2010) and Yasin et al. (2022) by incorporating spatial spillover effects at the provincial level. Subsequently, the authors test whether a firm’s technological intensity determines export–import propensity and intensity. This can indicate whether specific sectors are more likely to participate in international activities based on their use of technology.

In the actual livestreaming background, to obtain more income, some broadcasters will transform their original single role orientation into mixed one. This research study aims to conduct an empirical study on the influence of the broadcasters' role orientation transformation on the viewers' tipping behavior.

The authors collect data from Kuai, a leading online live streaming service provider in China. The dataset includes 175,701 live streaming data from 971 broadcasters in 7 months. To avoid unobservable factors, the authors adopt two difference-in-differences (DID) models to estimate the effect of two kinds of broadcaster's role orientation transformation on the broadcaster’s direct income separately. And the authors use the Heckman-type correction to solve broadcasters’ self-selected problem.

The authors evaluated that there is a U-shape relationship between the broadcasters' role orientation transformation and their direct income. The broadcasters' direct income experienced a sharp decline for a short period of time after transformation and followed by a rise after a period of adaptation. And for broadcasters with different genders and amounts of fans, the influence degree of role orientation transformation is various.

This paper provides a fresh usage of the regulatory engagement theory in the brand new information communication technology. And it also explores the boundary effect of the participating object's self-factors in the regulatory engagement theory. Besides, this paper expands the research of livestreaming into natural background. Such results also provide operable suggestions for the livestream platform, the broadcaster himself and the enterprises who want to employ some broadcasters to recommend their products.

Education system stimulates the development of human capital and provides informative signaling allowing to differentiate productivity of individuals. If education system is efficient then higher levels of education usually associated with greater returns on labor market. To evaluate the efficiency of Russian education system we aim to estimate the effect of vocational education and different levels of higher education on wages.

We use data on 8,764 individuals in the years 2019–2021. Our statistical approach addresses two critical issues: nonrandom selection into employment and the endogeneity of education choice. To tackle these problems, we employed Heckman’s method and its extension that is a structural model which addresses the issue of self-selection into different levels of education.

The results of the analysis suggest that there is a significant heterogeneity in the returns to different levels of education. First, higher education, in general, offers substantial wage premiums when compared to vocational education. Specifically, individuals with specialist’s and bachelor’s degrees enjoy higher wage premiums of approximately 23.59–24.04% and 16.43–16.49%, respectively, compared to those with vocational education. Furthermore, we observe a significant dis-parity in returns among the various levels of higher education. Master’s degree provides a substantial wage premium in comparison to both bachelor’s (19.79–20.96%) and specialist’s (12.64–13.41%) degrees. Moreover, specialist degree offers a 7.16–7.55% higher wage premium than bachelor’s degree.

We identify a hierarchical pattern in the returns associated with different levels of higher education in Russia, specifically “bachelor-specialist-master.” These findings indicate that each level of education in Russia serves as a distinct signal in the labor market, facilitating employers' ability to differentiate between workers. From a policy perspective, our results suggest the potential benefits of offering opportunities to transition from specialist’s to master’s degrees on a tuition-free basis. Such a policy may enhance access to advanced education and potentially lead to higher returns for individuals in the labor market.

There are many studies on returns to higher education in Russia. However, just few of them estimate the returns to different levels of higher education. Also, these studies usually do not address the issue of the endogeneity arising because of self-selection into different levels of education. Our structural econometric model allows addressing for this issue and provides consistent estimates of returns to different levels of education under the assumption that individuals with higher propensity to education obtain higher levels of education.

This research analyzes borrowers' credit utilization through prepayment behavior in peer-to-peer (P2P) lending. The authors investigate factors influencing the decision to prepay and assess the role of P2P lending as an alternative source of consumer credits.

The authors use individual loan-level data from the LendingClub, one of the largest P2P platforms in the USA. The authors use a Logit model and a sample selection model estimated by the two-stage Heckman method. The empirical analysis considers borrower-specific and loan-specific characteristics as well as macroeconomic factors.

The authors present a number of significant findings that can enhance understanding consumers' financing decisions. The authors offer evidence that borrowers are able to take advantage of cheaper loans offered by P2P lending to better manage credit card balance and consolidate debt. The authors find that borrowers tend to prepay P2P loans quickly when the aggregate cost of borrowing is low, suggesting that P2P lending offers an efficient alternative to obtain credit. This is particularly true for creditworthy borrowers that are able to take advantage of competing sources of finance. The authors' results provide evidence that P2P lending can improve consumers' optimal credit utilization.

P2P lending has grown exponentially and has become a significant credit supplier to consumers and small businesses. While the existing literature mostly focuses on default risks, prepayment has received much less attention. This research fills in the gap and investigates borrowers' prepayment behavior in P2P loans and the role of P2P lending as an alternative source of consumer credits.

This study aims to develop a model of learning-by-hiring in which knowledge gains may occur at the time of recruitment but also after recruitment when other incumbent organizational members assimilate a recruit’s knowledge. The author’s model predicts that experienced recruits are more likely to catalyze change to their organization’s core technological capabilities.

The continuous-time parametric hazard rate regressions predict core technological change in a long panel (1970–2017) of US biotechnology industry patent data. The author uses over 140,000 patents to model the evolution of knowledge of over 52,000 scientists and over 4,450 firms. To address endogeneity concerns, the author uses the Heckman selection method and does robustness tests using a difference-in-difference analysis.

The author finds that a hire’s prior research and development (R&D) experience helps overcome inertia arising from her or his new-to-an-organization “distant” knowledge to increase the likelihood of core technological change. In addition, while the author finds that incumbent organizational members resist technological change, experienced hires may effectively induce them to adopt new ways of doing things. This is particularly the case when hires collaborate with incumbents in R&D projects. Understanding the effects of hiring on core technological change, therefore, benefits from an assessment of hire R&D experience and its effects on incumbent inertia in an organization.

First, the author does not recommend managers to hire scientists with considerable distant knowledge only as this may be detrimental to core technological change. Second, the author recommends organizations striving to effectuate technological change to hire people with considerable prior R&D experience as this confers them with the ability to influence other members and socialize incumbent members. Third, the author recommends that managers hire people with both significant levels of prior experience and distant knowledge as they are complements. Finally, the author recommends managers to encourage collaboration between highly experienced hired scientists and long-tenured incumbent organizational members to facilitate incumbent learning, socialization and adoption of new ways of doing things.

This study develops a model of learning-by-hiring, which, to the best of the authors’ knowledge, is the first to propose, test and advance KM literature by showing the effectiveness of experienced hires to stimulate knowledge diffusion and core technological change over time after being hired. This study contributes to innovation, organizational learning and strategy literatures.