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The model of pay-for-knowledge incentivizes individuals with financial rewards for sharing their expertise, facilitating a transactional exchange between knowledge providers (sellers) and seekers (buyers). While this model is effective in promoting paid contributions, its influence on free knowledge exchanges remains ambiguous, creating uncertainty about its overall impact on platform knowledge ecosystems. This study aims to explore the mechanim of how knowledge payment influences free knowledge contribution. Based on relational signaling theory, this study posits that a buyer’s payment for knowledge acts as a positive relational signal in the buyer–seller relationship and examines how the signaling effect varies across different social contexts through attribution theory.

This paper empirically tests the hypotheses by analyzing a data set comprising 630 instances from 359 unique knowledge sellers on Zhihu, a prominent knowledge-sharing platform in China. This paper use zero-inflated negative binomial models to conduct this analysis.

The findings reveal that when buyers pay for knowledge, this action positively influences sellers to contribute knowledge for free. However, the strength of this influence is moderated by the platform’s social functions: appreciation feedback tends to weaken this effect, while social network ties enhance it.

Prior research has predominantly focused on the financial incentives of pay-for-knowledge and its spillover effects on unpaid users’ activities. This study shifts the focus to the social dimensions of pay-for-knowledge, arguing that buyer-initiated knowledge payments signal buyers’ commitment to foster reciprocal relationships with sellers. It expands the literature on the relationship between knowledge payment and contribution, moving beyond financial incentives to include social factors, thus enriching our understanding of the interplay between paid and free knowledge activities. Additionally, the empirical evidence supports the efficacy of pay-for-knowledge in promoting both free and paid contributions within knowledge-sharing platforms.

The study investigates the impact of various factors on the number of active investors in digital health startups. Through nine hypotheses, we examine the influence of metrics such as patents, online presence, financial aspects and company valuation on investor interest. The results reveal positive associations between these metrics and investor numbers, highlighting their role in signaling strength and attracting investment. This research enhances the understanding of investor valuation in digital health startups, emphasizing the importance of credible signals for building trust and securing funding. However, we acknowledge limitations in data analysis methods and suggest future research to explore industry signals, longitudinal trends and failed startups for comprehensive insights.

This study delves into the design methodology and approach, aiming to fill gaps in understanding investor roles in valuing digital health ventures. We focus on deciphering factors driving valuations for these startups to secure growth financing. Using signaling theory, we investigate how entrepreneurs communicate their latent strengths to bridge information gaps, aiding investment decisions. We analyze a sample of 482 healthcare startups from the Pitchbook database using Poisson regression in SPSS.

This research sheds light on the factors driving investor interest in digital health startups. Despite the critical role of entrepreneurs in patient care innovations, the relationship between investor characteristics and funding for digital health technologies still needs exploration. We examine factors influencing investor valuation in healthcare startups and identify patents, social followers and financial disclosures as pivotal elements shaping investor interest. The findings show that all factors for active investors are significant for all variables except similar unique visitors.

These results significantly enhance our understanding of investor decision-making in digital health startups. They confirm the importance of various signals, like patent activity, online presence and financial performance, in attracting investor attention. We utilize unique data sources, offering insights into investors' behavior across different funding stages. In conclusion, these findings underscore investors' crucial role in the growth and funding of healthcare tech startups, emphasizing the need for robust signals to attract investment.

This chapter reviews the econometric approaches typically used to deal with the spike of zeros when modelling non-negative outcomes such as expenditures, income, or consumption. Relying on the assumptions of selection on observables for evaluating a policy or treatment, this chapter discusses other issues that arise with spikes of zeros in the data, including the analyst's choice between full information versus quasi-likelihood methods, considering whether observed zeros are true or masking more complex behavioural decisions, and dealing with zeros that arise due to self-selection. This chapter ends with discussions of empirical strategies to deal with these behavioural assumptions and a brief review of the literature where such strategies were employed.

The purpose of this paper is to investigate the importance of culture as a motivator for young generations to travel and their willingness of using crowdfunding to sustain culture.

Using a mixed-methods sequential exploratory design and through a quantile regression analysis for count data, a sample of 1,721 Italian young people is examined.

The analysis reveals that culture is a significant factor for a trip’s motivation among young generations and crowdfunding represents a key alternative instrument for financing culture.

The research fills the gap in extant literature by clarifying the role of culture in the choice of a touristic destination by young generations. This is a significant achievement since understanding the motivations is crucial to attract tourists at a specific destination and it represents a relevant insight for policy makers.

The purpose of this study is to investigate the influence of external enablers (EEs) – factors that are exogenous and independent of the actors – on the rate of new venture creation (NVC) in digital entrepreneurship.

The authors established a unique data panel comprising all formal digital entrepreneurial firms and startups licensed to operate in the digital space from Iran E-Commerce Development Center, from 2012 to 2022, totaling 124,751 businesses classified in 16 sectors. The authors used Poisson regression with robust standard errors to analyze the relationship between external factors and NVC. Additionally, the authors segmented the data set along demographic, spatial and sectoral subsets to compare them using the average treatment effect model.

This analysis confirms that technology readiness and technology intensity, as EEs, significantly boost digital entrepreneurship in a developing country. The authors also explored the impact of the COVID-19 pandemic as a natural environmental enabler and explored its facilitating effects and the enduring impacts post-pandemic. Moreover, the authors assessed the heterogeneous impact of the EEs across different groups and derived implications for each.

While this study is comprehensive, it focuses exclusively on formal digital entrepreneurship within a single developing country, namely, Iran. Although the authors explored various EEs, they did not address all possible types, highlighting the potential for further research in other entrepreneurial sectors for a comparative analysis of EEs.

The initial findings affirm that most NVC in Iran stems from external factors. These findings have been widely used by policymakers to define the goals and objectives of EEs concerning their nature and impact. This research reveals that EEs have heterogeneous effects on NVC, varying across different characteristics such as gender, geography and industry sectors. The results of this study suggest that governments should reassess the role of “macro” policies to support vulnerable groups. EEs disproportionately benefit male founders, which exacerbates existing gender gaps and necessitates ongoing attention and intervention. Additionally, EEs have diverse impacts across industries, potentially favoring some while disadvantaging others because of varying social, cultural and economic factors.

NVC, central to entrepreneurship in developing nations, encounters numerous challenges, yet the role of EEs remains underexplored in this context. EEs are opportunities that are not limited to a subset of entrepreneurial actors; they act as levers influencing a wide range of entrepreneurial activities. This research distinctively investigates the empirical impact of EEs in a Middle Eastern developing country context, contributing to the growing literature on the external enablers framework.

This paper investigates one of the potential costs of rising segregation in American cities by evaluating empirically the extent at which ethnic-based segregation contributes to the onset and the speed of propagation of the COVID-19 pandemic.

Regression analysis based on matched data on early incidence of COVID-19 cases, segregation and covariates. Identification resorts on variations in segregation across MSAs and heterogeneity in the geography and timing of stay-at-home orders.

One cross-MSA standard deviation increase in segregation leads to a significant and robust rise of COVID-19 cases of 8.7 per 100,000 residents across urban counties.

Combines spatial data on COVID-19 cases and segregation; use of a new segregation measure; focus on early incidence of the pandemic and its drivers.

Building on current debates on innovation, knowledge diffusion, and institutional dynamics, we explore the influence of national innovation systems (NISs) on international innovation collaborations in Latin America, focusing on intellectual property rights (IPR), access to scientific knowledge and regulatory quality.

We analyze data from 17 Latin American countries from 2002–2015 using time-series panel analysis to evaluate how different NIS elements affect regional cooperation for innovation.

Regulatory quality can improve international collaboration by compensating for weaker IPR and scientific knowledge bases. Interestingly, while both IPR and scientific knowledge inherently promote cooperation, stronger regulatory environments may diminish the effectiveness of IPR protections, suggesting a potential substitution effect.

The study offers actionable insights for policymakers in developing regions to help them craft more effective policies for collaboration in innovation that consider the balancing act between regulatory quality and other NIS elements.

This research shifts focus from the conventional analysis of how developing countries attract collaboration from developed nations to how they can foster innovation among themselves, providing a unique perspective on the interaction between institutional factors and innovation capabilities within the Latin American context.

The purpose of this study is to investigate whether and how inter-firm cooperation and firm age moderate the relationship between family ownership and productivity.

We first estimate the total factor productivity (TFP) of a large sample of Italian firms observed over the period 2010–2018 and then apply a Poisson random effects model.

TFP is, on average, higher for non-family firms (non-FFs) than for FF. Furthermore, inter-organizational cooperation and firm age mitigate the negative effect of family ownership. In detail, it is found that belonging to a network acts as a moderator in different ways according to firm age. Indeed, young FFs underperform non-FF peers, although the TFP gap decreases with age. In contrast, the benefits of a formal network are high for older FFs, suggesting that an age-related learning process is at work.

The study provides evidence that FFs can outperform non-FFs when they move away from Socio-Emotional Wealth-centered reference points and exploit knowledge flows arising from high levels of social capital. In the case of mature FFs, networking is a driver of TFP, allowing them to acquire external resources. Since FFs often do not have sufficient in-house knowledge and resources, they must be aware of the value of business cooperation. While preserving the familiar identity of small companies, networks grant FFs the competitive and scale advantages of being large.

Despite the wide but ambiguous body of research on the performance gap between FFs and non-FFs, little is known about the role of FFs’ heterogeneity. This study has proven successful in detecting age as a factor in heterogeneity, specifically to explain the network effect on the link between ownership and TFP. Based on a representative sample, the study provides a solid framework for FFs, policymakers and academic research on family-owned companies.

Organisational slack has been widely considered in strategic management, but there is a gap in understanding the process of accumulation and application of slack resources. From a dynamic perspective and over an extended period of time, this paper analyses the management of slack resources and evaluates whether the different behaviours, in relation to the accumulation and consumption of slack resources, have any effect on performance.

The resource-based view and the dynamic extension of this theory, i.e. resource management and resource orchestration, were analysed in order to evaluate how slack resources can be managed and generate value. Assuming a configurational approach, the analysis was structured into two stages to answer the proposed hypothesis. The first stage studied whether there were different patterns of management of slack resources over time using the DistatisR package. The second stage evaluated which behaviours had the greatest impact in terms of profitability by using a dynamic panel data regression.

Three different types of slack resource management were found in companies: efficient, effective and erratic. Different types do not have the same impact on performance.

The dynamic management of slack resources has scarcely been considered, even during periods of crisis and economic expansion. This research advances the understanding of how firms transform slack resources into performance from a dynamic perspective.

The COVID-19 pandemic dramatically affected the fire service: stay-at-home orders and potential exposure hazards disrupted standard fire service operations and incident patterns. The ability to predict incident volume during such disruptions is crucial for dynamic and efficient staff allocation planning. This work proposes a model to quantify the relationship between the increase in “residential mobility” (i.e. time spent at home) due to COVID-19 and fire and emergency medical services (EMS) call volume at the onset of the pandemic (February – May 2020). Understanding this relationship is beneficial should mobility disruptions of this scale occur again.

The analysis was run on 56 fire departments that subscribe to the National Fire Operations Reporting System (NFORS). This platform enables fire departments to report and visualize operational data. The model consists of a Bayesian hierarchical model. Text comments reported by first responders were also analyzed to provide additional context for the types of incidents that drive the model’s results.

Overall, a 1% increase in residential mobility (i.e. time spent at home) was associated with a 1.43% and 0.46% drop in EMS and fire call volume, respectively. Around 89% and 21% of departments had a significant decrease in EMS and fire call volume, respectively, as time spent at home increased.

A few papers have investigated the impact of COVID-19 on fire incidents in a few locations, but none have covered an extensive number of fire departments. Additionally, no studies have investigated the relationship between mobility and fire department call volumes.