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The COVID-19 pandemic, a sudden and disruptive external shock to the USA and global economy, profoundly affected various operations. Thus, it becomes imperative to investigate the repercussions of this pandemic on the US housing market. This study investigates the impact of the COVID-19 pandemic on a crucial facet of the real estate market: the Time on the Market (TOM). Therefore, this study aims to ascertain the net effect of this unprecedented event after controlling for economic influences and real estate market variations.

Monthly time series data were collected for the period of January 2010 through December 2022 for statistical analysis. Given the temporal nature of the data, we conducted the Durbin–Watson test on the OLS residuals to ascertain the presence of autocorrelation. Subsequently, we used the generalized regression model to mitigate any identified issues of autocorrelation. However, it is important to note that the response variable derived from count data (specifically, the median number of months), which may not conform to the normality assumption associated with standard regression models. To better accommodate this, we opted to use Poisson regression as an alternative approach. Additionally, recognizing the possibility of overdispersion in the count data, we also explored the application of the negative binomial model as a means to address this concern, if present.

This study’s findings offer an insightful perspective on the housing market’s resilience in the face of COVID-19 external shock, aligning with previous research outcomes. Although TOM showed a decrease of around 10 days with standard regression and 27% with Poisson regression during the COVID-19 pandemic, it is noteworthy that this reduction lacked statistical significance in both models. As such, the impact of COVID-19 on TOM, and consequently on the housing market, appears less dramatic than initially anticipated.

This research deepens our understanding of the complex lead–lag relationships between key factors, ultimately facilitating an early indication of housing price movements. It extends the existing literature by scrutinizing the impact of the COVID-19 pandemic on the TOM. From a pragmatic viewpoint, this research carries valuable implications for real estate professionals and policymakers. It equips them with the tools to assess the prevailing conditions of the real estate market and to prepare for potential shifts in market dynamics. Specifically, both investors and policymakers are urged to remain vigilant in monitoring changes in the inventory of houses for sale. This vigilant approach can serve as an early warning system for upcoming market changes, helping stakeholders make well-informed decisions.

The study examines the relationship between news intensity, media sentiment and market microstructure invariance-implied measures of trading activity and liquidity of Chinese property developer stocks during the 2020–2022 Chinese property sector crisis.

The authors adopt the extension of the news article invariance hypothesis, which is a generalization of the market microstructure invariance conjecture, from January 2020 to January 2022 to test specific quantitative relationships between the arrival rate of public information, trading activity and a nonlinear function of a proxy for the probability of informed trading. Empirical tests are based on a dataset of 22,412 firm-day observations and two count-data models to correct for overdispersion and the excess number of zeros. Seventy-five stocks of Chinese companies from the property development industry (including the China Evergrande Group) were included in the sample.

The authors reject the news article invariance hypothesis but document a positive and significant relationship between the flow of public information and risk liquidity. Additionally, the authors find that the proxy for informed trading activity is positively related to the arrival rates of public information from October 2021 to January 2022.

The findings support the hypothesis that negative (positive) media sentiment induces significant deterioration (insignificant improvement) in stock liquidity. The authors find that an increase in the number of news articles about a company corresponds to a higher liquidity of Chinese property developers' stocks after controlling for media sentiment.

To remain sustainable, open source software (OSS) projects must attract new members—or newcomers—who make contributions. In this paper, the authors develop a set of hypotheses based on the knowledge barriers framework that examines how OSS communities can encourage contributions from newcomers.

Employing longitudinal data from the source code repositories of 232 OSS projects over a two-year period, the authors employ a Poisson-based mixed model to test how community characteristics, such as the main drivers of knowledge-based costs, relate to newcomers' contributions.

The results indicate that community characteristics, such as programming language choice, documentation effort and code structure instability, are the main drivers of knowledge-based contribution costs. The findings also suggest that managing these costs can result in more inclusive OSS communities, as evidenced by the number of contributing newcomers; the authors highlight the importance of maintaining documentation efforts for OSS communities.

This paper assumes that motivational factors are a necessary but insufficient condition for newcomer participation in OSS projects and that the cost to participation should be considered. Using the knowledge barriers framework, this paper identifies the main knowledge-based costs that hinder newcomer participation. To the best of the authors' knowledge, this is the first empirical study that does not limit data collection to a single hosting platform (e.g., SourceForge), which improves the generalizability of the findings.

This study aims to investigate the influence of the reviewed establishment’s price level and the user’s social network size and reputation status on consumers’ tendency to post more visual imagery content. Furthermore, it explores the moderating effects of user experiences and geographic distance on these dynamics.

This study adopts a multi-method approach to explore both the determinants behind the sharing of user-generated photos in online reviews and their internal mechanisms. Using a comprehensive secondary data set from Yelp.com, the authors focused on restaurant reviews from a prominent tourist destination to construct econometric models incorporating time-fixed effects. To enhance the robustness of the authors’ findings, the authors complemented the big data analysis with a series of controlled experiments.

The reviewed establishments price level and the users reputation status and social network size incite corresponding motivations conspicuous display “reputation seeking” and social approval motivating users to incorporate more images in reviews. “User experiences can amplify the influence of these factors on image sharing.” An increase in the users geographical distance lessens the impact of the price level on image sharing, but it heightens the influence of the users reputation and social network size on the number of shared images.

As a result of this study, high-end establishments can increase their online visibility by leveraging user-generated visual content. A structured rewards program could significantly boost engagement by incentivizing photo sharing, particularly among users with elite status and extensive social networks. Additionally, online review platforms can enhance users’ experiences and foster more dynamic interactions by developing personalized features that encourage visual content production.

This research, anchored in trait activation theory, offers an innovative examination of the determinants of photo-posting behavior in online reviews by enriching the understanding of how the intricate interplay between users’ characteristics and situational cues can shape online review practices.

This study aims to provide empirical evidence on two research questions: firstly, whether green finance is positively related to electricity access, and, secondly, if the domestic economic environment moderates the relationship between green finance and electricity access? This paper pays particular attention to the regional disparities in Africa.

While pursuing the study objectives, the authors apply a variety of statistical approaches and tools to assess the robustness of the findings. The authors use panel dataset for analysing data. In order to empirically examine the relationship between green finance and electricity access in the African region, the paper employs static and dynamic panel estimation methods, Poisson method and adopts two-step system generalized method of moments (GMM) approach for dealing with issues relating to endogeneity. The authors also use alternate proxy for the electricity access, which is drawn from the regulatory indicators for sustainable energy (RISE) scores.

The authors find that despite the fact that green funding appears to support job creation, household incomes aren't high enough to drive rising demand for electricity. The study underscores the role and responsibilities of external funding agencies to ensure that funds at the receiving end are effectively routed to encourage access to clean and sustainable energy, which is good to the economic and domestic environment. Further, due to the relatively modest size of some funds, the cost to administer those funds is larger than the funds themselves. This causes inefficiencies, which may temporarily provide jobs but not lasting growth. This means there is no regular need for energy, therefore larger investors have no reason to enter the market. This discourages investors from public-private partnerships or private investments and prevents future investment.

The provide insights into the private-public partnerships and whether the challenges to electricity access are being turned into investment opportunities. The effects of the power Africa project initiatives are revealing, with, sanitation being an impediment to the development of electricity infrastructure, specifically in low-income group countries.

The study confirms the view that trivial amounts of green financing (US-Aid or grants) impose a burden on the absorptive capacity of the recipient government and increases the transaction costs and is likely to be an impediment (Kimura et al., 2012) to initiating projects that enhance electricity access.

The results indicate that although green financing seems to be supporting employment opportunities, income levels are insufficient to create demand for electricity usage. It, therefore, becomes imperative that sanitation (SDG 6) is fully addressed in order to ensure that SDG 7 is attained.

The authors provide insights around the private public partnerships and whether the challenges to electricity access are being turned into investment opportunities. The effects of the power Africa project initiatives are revealing, with, sanitation being an impediment to the development of electricity infrastructure, specifically in low-income group countries.

The economic crisis of 2008 resulted in a unique context for the empirical experimentation of organizational learning and entrepreneurship theories from Schumpeterian inspiration. The purpose of this paper is to investigate how the economic downturns change the limits and possibilities to pursue both exploration and exploitation and thus achieve innovative performance.

This study examines an original database with the patent portfolio of 2,309 firms based in Brazil, covering a period of eight years preceding the 2008 economic crisis, that is 2000–2007, and five years thereafter, 2008–2013. To test the hypotheses empirically, the authors use the negative binomial model.

The findings add to the literature in two ways. First, this study found a positive association between exploitation and exploration behaviour and innovative performance. However, this relation is curvilinear because there are diminishing negative effects on innovation. Second, regarding the distinction between creative destruction and creative accumulation, the results support the prevalence of creative accumulation, although consistent with the notion of co-occurrence of both models.

This study expands the discussion on how entrepreneurs respond to the economic crisis in specific contexts of an emerging economy. We provide new empirical evidence through the exploration of a unique data set on the patent portfolio of 2,309 firms based in Brazil. Most of the previous literature has applied this topic to developed countries, and there are few studies that apply this topic outside the focus of US and European countries.

The purpose of this paper is to investigate the role of firms’ features on innovation performance in Industry 4.0, focusing on the concepts of breadth and depth of openness in the innovation process.

Using data gathered from 96 firms active in Industry 4.0 (I4.0) in Italy, a Poisson regression analysis is conducted to investigate the relationship between the openness of firms’ innovation processes at the level of knowledge sources and their innovation performance in I4.0.

The results highlight the relationship between the level of openness and innovative performance in I4.0. In particular, the breadth of the openness of the innovation process of enterprises is curvilinearly related to innovation in I4.0, taking an inverted U-shape.

Managers of firms operating in I4.0 should consider openness as a strategic response to the knowledge requirements and risks associated with the innovation process in complex technologies.

Through the questionnaires administered mainly to highly qualified individuals, an original and unique database has been created with information on the openness of the innovative process and the innovation performances in I4.0.

A public–private partnership (PPP) is an agreement between the government and private investors to deliver long-term public services. The efficiency of PPP projects depends on PPP contracts stipulating contractual parties' corresponding responsibilities and rights to deal with relational and performance risks. Although more complex contracts provide more remedies for mitigating ex-post transaction costs, they also result in the increased ex ante transaction costs associated with contract writing. Thus, contractual complexity is a design choice that can reduce the overall contract transaction costs.

Using 365 transportation PPP projects in China from 2010 to 2019, this study applies the Poisson regression model to examine the effects of payment mechanisms, ownership by investors and equity structure on contractual complexity.

PPP contracts have control and coordination functions with unique determinants. Parties in the government-pay mechanism are more likely to negotiate coordination provisions, which results in greater contractual complexity. PPP projects with state-owned enterprises (SOEs) have less contractual complexity in terms of both two functions of provisions, whereas the equity structure has no impact on contractual complexity.

These findings provide a nuanced understanding of how various contractual provisions are combined to perform control or coordination functions and make managerial recommendations to parties involved in PPP projects.

This paper aims to explore the effect of teacher–student collaboration on academic innovation in universities in different stages of collaboration.

Based on collaboration life cycle, this paper divided teacher–student collaboration into initial, growth and mature stages to explore how teacher–student collaboration affects academic innovation.

Collecting data from National Science Foundation of China, the empirical analysis found that collaboration increases the publication of local (Chinese) papers at all stages. However, teacher–student collaboration did not significantly improve the publication of international (English) papers in the initial stage. In the growth stage, teacher–student collaboration has a U-shaped effect on publishing English papers, while its relationship is positive in the mature stage.

The results offer suggestions for teachers and students to choose suitable partners and also provide some implications for improving academic innovation.

This paper constructed a model in which the effect of teacher–student collaboration on academic innovation in universities was established.

Change orders are a typical occurrence in building projects. Change orders indirectly affect labor productivity, resulting in a significant delay in the completion of a building project. Change orders cause labor productivity losses that are difficult to describe, establish and account for contractors and subcontractors. This study aimed to look at the influence of change orders on labor productivity and develop methods to mitigate their adverse effects.

To assess the change orders' impact on productivity levels a system dynamic model was developed and devise ways were developed to counteract these negative impacts in this research. The impact of change orders on labor productivity and project time was then controlled using techniques established. Finally, a case study of KUET's hall extension was chosen, and the model and principles developed were implemented.

This study established that if the project delivery date is set and change orders are occurring often, labor productivity will be impacted. With adequate monitoring and supplemental management techniques, it can be reduced by prolonging the project.

The developed policies aid to mitigate the effect of change orders on labor productivity.