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Users' voluntary forwarding behavior opens a new avenue for companies to promote their brands and products on social networking sites (SNS). However, research on voluntary information disseminators is limited. This paper aims to bring an in-depth understanding of voluntary disseminators by answering the following questions: (1) What is the underlying mechanism by which some users are more enthusiastic to voluntarily forward content of interest? (2) How to identify them? We propose a theoretical model based on the Elaboration-Likelihood Model (ELM) and examine three types of factors that moderate the effect of preference matching on individual forwarding behavior, including personal characteristics, tweet characteristics and sender–receiver relationships.

Via Twitter API, we randomly crawled 1967 Twitter users' data to validate the conceptual framework. Each user’s original tweets and retweeted tweets, profile data such as the number of followers and followees and verification status were obtained. The final corpus contains 163,554 data points composed of 1,634 valid twitterers' retweeting behavior. Tweets produced by these core users' followees were also crawled. These data points constitute an unbalanced panel data and we employ different models — fixed-effects, random-effects and pooled logit models — to test the moderation effects. The robustness test shows consistency among these different models.

Preference matching significantly affects users' forwarding behavior, implying that SNS users are more likely to share contents that align with their preferences. In addition, we find that popular users with lots of followers, heavy SNS users who author tweets or forward other-sourced tweets more frequently and users who tend to produce longer original contents are more enthusiastic to disseminate contents of interest. Furthermore, interaction strength has a positive moderating effect on the relationship between preference matching and individuals' forwarding decisions, suggesting that users are more likely to disseminate content of interest when it comes from strong ties. However, the moderating effect of perceived affinity is significantly negative, indicating that an online community of individuals with many common friends is not an ideal place to engage individuals in sharing information.

This work brings about a deep understanding of users' voluntary forwarding behavior of content of interest. To the best of our knowledge, the current study is the first to examine (1) the underlying mechanism by which some users are more likely to voluntarily forward content of interest; and (2) how to identify these potential voluntary disseminators. By extending the ELM, we examine the moderating effect of tweet characteristics, sender–receiver relationships as well as personal characteristics. Our research findings provide practical guidelines for enterprises and government institutions to choose voluntary endorsers when trying to engage individuals in information dissemination on SNS.

This study aims to investigate the implications of natural gas rents and institutions as co-drivers of economic growth, focusing on the Gas Exporting Countries Forum (GECF) with panel data between 2001 and 2021.

This research paper uses a specialised two stage estimator, the panel instrumental variable technique (panel IV), which takes care of the potential endogeneity issues in the model.

The findings show that natural gas rent significantly impacts the economic growth of the GECF. On average, natural gas rent increases the sample’s growth rate by about 2.634% percentage points in the short run. The result indicates that the qualities of institutions (political and economic) have a significant positive long-term effect on the economies of the GECF. In addition, the study’s energy price volatility positively correlates with the countries’ growth.

There might be a need to investigate the effects of natural gas rents and institutions as co-growth drivers in each country within the GECF. The likelihood exists that the impact of natural gas rents and institutions on economic growth at the country’s level may differ from the outcome of such an experiment on the group level. Because of space and time limitations, this study could not carry out the specific country’s investigation of natural gas rents and institutions as a co-growth driver. That limitation may constitute further study to advance this study to a new height.

With good institutions, natural gas rent is likely to be an alternative growth driver for some economies that rely on fossil fuels like oil as a growth driver. By extension, the GECF has the potential to rival Organisation of Petroleum Exporting Countries (OPEC) in the global energy market, particularly in achieving Sustainable Development Goal number seven. In essence, evidence in this study suggests that natural gas rent has long-term positive effects on the growth of the GECF, conditioned on good institutions. Moreover, the drive of global energy consumption towards sustainable energy usage is an economic blessing for the GECF. By extension, the demand for natural gas would continue to rise, creating opportunities to improve natural gas rents. By implication, the GECF would continue to benefit from the pursuit of sustainability as the world shifts towards energy consumption with less CO₂.

Firstly, this study models the qualities of institutions for the GECF. Secondly, to the best of the author’s knowledge, this study is the first attempt to examine natural gas rents and the qualities of institutions as co-determinants of economic growth among the GECF (a potential cartel).

The crux of this paper is to unveil efficient features and practical tools that can predict credit default.

Annual data of non-financial listed companies were taken from 2000 to 2020, along with 71 financial ratios. The dataset was bifurcated into three panels with three default assumptions. Logistic regression (LR) and k-nearest neighbor (KNN) binary classification algorithms were used to estimate credit default in this research.

The study’s findings revealed that features used in Model 3 (Case 3) were the efficient and best features comparatively. Results also showcased that KNN exposed higher accuracy than LR, which proves the supremacy of KNN on LR.

Using only two classifiers limits this research for a comprehensive comparison of results; this research was based on only financial data, which exhibits a sizeable room for including non-financial parameters in default estimation. Both limitations may be a direction for future research in this domain.

This study introduces efficient features and tools for credit default prediction using financial data, demonstrating KNN’s superior accuracy over LR and suggesting future research directions.

Findings in the literature indicate leading financial analysts attract high levels of market attention and provide more accurate earnings forecasts prior to becoming all-star analysts. Furthermore, these analysts significantly impact the investment decisions of other market participants and thus the market price of assets. Therefore, this study examines the information role of leading financial analysts and identifies two significant conclusions. First, the positive outcomes of these analyst leaders are more informative and attract more followers. Second, informational herding by followers of these analysts is not as naïve as suggested in previous studies, as followers who smartly use information from analyst leaders tend to perform better. We also find that analysts who practice smart learning by studying and selectively employing analyst-leader decisions achieve better career outcomes.

This study aims to study the moderating effects of private ownership and government support on the relationship between outsourcing and product innovation in entrepreneurial ventures in a transition economy.

The data of 10,296 Vietnamese entrepreneurial ventures from the four rounds of the survey conducted by the General Statistics Office (GSO) of Vietnam to investigate the moderating effects of private ownership and government support on the association between outsourcing and entrepreneurial ventures’ product innovation performance. The Probit regression model is employed to estimate such associations.

Our research uncovered that the impact of outsourcing on the likelihood of product innovation is more significant for entrepreneurial operations characterized by a substantial degree of private ownership and government backing as opposed to those without.

The results of our research indicated that the resource-based perspective and extended resource-based view (ERBV) are essential in examining the impact of gaining resources or skills from external sources on the growth of entrepreneurial enterprises. These ideas have significance and importance not just in industrialized economies but also in countries undergoing transition. Our findings suggest that entrepreneurial enterprises should have the ability to manage a wide range of resources and make decisions about which activities should be handled internally and which should be delegated to other parties.

Our findings also imply that entrepreneurial ventures should be able to control many resources and choose which tasks should be performed in-house and which should be outsourced to third parties.

By adopting and leveraging the resource-based view (RBV) and extended resource-based views (ERBV), our study developed a theoretical model about private ownership and government support for moderate outsourcing’s impact on entrepreneurial innovation in a transition economy.

This study explores whether herd behavior exists for equity crowdfunding investors in China and whether this herding is rational.

Based on signaling theory and social learning theory, two hypotheses were proposed. This study employed two approaches to collect data. First, this paper analyzed 3,041 investments on an equity crowdfunding platform in China using Python programming and built a panel data model. Second, based on a unique experiment design, this study conducted several relevant herd behavior simulation experiments.

We found that investors in the Chinese equity crowdfunding market exhibit herd behavior and that this herding is rational. Project attributes play a negative role in moderating the relationship between the current investment amount and cumulative investments. Experimental results further support our findings.

This study contributes to the emerging literature on herding in crowdfunding by focusing on equity crowdfunding in China. We are the first to explore whether Chinese equity crowdfunding investors exhibit rational herding behavior. The study is also original in applying social learning theory to equity crowdfunding and in using both actual crowdfunding campaigns and experimental approaches to collect data. This study has valuable implications to practice.

This study employs firm-level data to evaluate how the knowledge economy impacts the financing constraints of businesses across 106 low- and middle-income nations, focusing on the influence of technological transformation on corporate financing choices.

The research centers on privately held, unlisted firms and examines the distinct effects of knowledge at both the within-country and between-country levels using a panel dataset. Rigorous sensitivity and endogeneity analyses are conducted to ensure the reliability of the findings.

The findings indicate that greater levels of the knowledge economy correlate with reduced financing constraints for firms. However, this effect varies depending on the location within a country and across different geographical regions. Firms situated in larger urban centers and more innovative regions reap the most significant benefits from the knowledge economy when seeking external funding. Conversely, firms in smaller cities, rural areas and regions characterized by structural and institutional inefficiencies in knowledge generation experience fewer advantages.

The impact of knowledge exhibits variability not only within and among countries but also between poor and affluent developing nations, as well as between larger and smaller countries. The knowledge effect on firms' access to external finance is influenced by factors such as financial openness and development, educational quality, technological absorption capabilities and agglomeration conditions within each country.

The purpose of this study is to investigate the association between nonaudit services (NAS) and out-of-period adjustments (OOPAs). Over the years, the number of OOPAs has risen while the number of restatements has decreased. This could indicate an improvement in financial reporting quality. It could also indicate the use of a type of stealth restatement for opportunistic purposes. These less prominent restatements are more likely to go undetected and could perpetuate opportunistic disclosure and mitigate the likelihood of unfavorable market reactions.

The authors use a two-stage multivariate regression analysis to examine the relationship between NAS and the reporting of an OOPA. The authors use prior research on NAS to guide the model development. The authors perform several robustness checks including different types of NAS and different characteristics of OOPAs.

The results indicate that NAS has a significantly negative association with the existence of OOPAs. The core findings suggest that NAS does not impair auditor independence. Rather, greater amounts of NAS may contribute to knowledge spillover, which leads to higher financial reporting and audit quality. The results are robust to several additional tests.

The results raise interesting implications for regulators, executives, auditors, investors and future research. The authors provide insight into the relationship between NAS and auditor independence.

To the best of the authors’ knowledge, prior research has not considered the effect of NAS on OOPAs. The authors contribute to the literature by providing evidence that OOPAs, a form of stealth restatements, is an important consideration in audit quality research.

Open innovation (OI), by now one of the major concepts for the analysis of innovation, is seen as a methodology for collaboratively designing and implementing solutions by engaging stakeholders in an iterative and inclusive service design process. This paper aims to empirically investigate OI capacities, defined as a cooperative, knowledge-sharing innovation ecosystem, and to explore how it can lead to improved performance of firms in Central and Eastern European (CEE) and Southeastern European (SEE) countries.

The study builds on the World Bank/European Bank for Reconstruction and Development (EBRD’s) Business Environment Enterprise Performance Survey (BEEPS) dataset for 2009, 2013 and 2019. Primarily, the research model was estimated using log-transformed ordinary least squares (OLS). Taking into consideration that this method might produce substantial bias, yielding misleading inferences, this study is fitting Poisson pseudo maximum likelihood estimators with robust standard errors and instrumental variable/generalized method of moments estimation (IV/GMM) approach for comparative results. Secondarily, the research model was tested using structural equation modelling (SEM) to investigate the relationship between five OI capacities and firm performance.

The findings indicate that there is a significant positive relationship between most OI capacities and firm performance, except for innovation, which did not show a statistically significant relationship with firm performance. Specifically, research and development (R&D), knowledge and coopetition are statistically significant and positively associated with firm performance, whereas transformation is statistically significant but negatively associated with firm performance. The IV/GMM estimations’ findings support the view that the firm performance is significantly affected by OI capacities, together with some control variables such as size, age, foreign ownership and year dummy to have a significant impact on firm performance.

This paper fills an identified gap in the literature by investigating the impact of OI on firm performance executed in the specific CEE and SEE country context.

The purpose of this study is to examine the impact of mandatory corporate social responsibility (CSR) spending on firm value in the Indian context.

Using firm-level data over the period 2012–2017, this study uses the difference-in-differences (DID) technique combined with matching to control for potential endogeneity of the decision to comply with the CSR Act since the Act in its current form is applicable as a comply-or-explain obligation.

The results of this study suggest that mandatory CSR spending has a positive and statistically significant impact on firm value. These results remain robust to alternative econometric techniques such as regression discontinuity design (RDD) and randomization inference test as well as to alternative empirical specifications. Furthermore, the study demonstrates that the positive effect of CSR spending on firm value is more pronounced for firms with higher information asymmetry problem and lower institutional holdings.

This study explicitly considers the “comply-or-explain” flexibility option, in terms of spending on CSR, provided to Indian firms for the initial two to three years and investigates whether spending on CSR helps firms enhance their firm value. The study also finds that the positive effect of CSR spending on firm value is more pronounced for firms with higher information asymmetry problems and lower institutional holdings.