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Positive reviews can enrich the favorable impression of peer-to-peer accommodation products, and seizing this impression is vital for hosts. This study aims to focus on hosts’ response strategies to positive reviews and their effects.

This study categorizes hosts’ response strategies to positive reviews into cordial and tailoring responses. This study empirically analyzes the influence of these response strategies on subsequent review volumes using 1,283 valid listings and zero-inflation negative binomial regression models.

While hosts use cordial responses more, tailoring responses are more likely to drive subsequent reviews. In addition, when the host chooses entirely shared accommodation or sets a high price, the facilitating effect of the two response strategies on subsequent reviews weakens.

This study enriches the knowledge system on managerial responses by proposing two specific response strategies to positive reviews that can be adopted by peer-to-peer accommodation hosts and by finding the promoting impact of these strategies on subsequent review volumes.

This study recommends that peer-to-peer accommodation hosts adopt cordial and tailoring responses to encourage subsequent consumer reviewing behavior.

As an early attempt to explore hosts’ responses to positive reviews and their impacts on subsequent review volumes, this study provides valuable insights into further research on positive review response strategies in the digital space.

Despite large bodies of research related to the impacts of e-commerce on last-mile logistics and sustainability, there has been limited effort to evaluate urban freight using an equity lens. Therefore, this study proposes a modeling framework that enables researchers and planners to estimate the baseline equity performance of a major e-commerce platform and evaluate equity impacts of possible urban freight management strategies. The study also analyzes the sensitivity of various operational decisions to mitigate bias in the analysis.

The model adapts empirical methodologies from activity-based modeling, transport equity evaluation, and residential freight trip generation (RFTG) to estimate person- and household-level delivery demand and cargo van traffic exposure in 41 U.S. Metropolitan Statistical Areas (MSAs).

Evaluating 12 measurements across varying population segments and spatial units, the study finds robust evidence for racial and socio-economic inequities in last-mile delivery for low-income and, especially, populations of color (POC). By the most conservative measurement, POC are exposed to roughly 35% more cargo van traffic than white populations on average, despite ordering less than half as many packages. The study explores the model’s utility by evaluating a simple scenario that finds marginal equity gains for urban freight management strategies that prioritize line-haul efficiency improvements over those improving intra-neighborhood circulations.

Presents a first effort in building a modeling framework for more equitable decision-making in last-mile delivery operations and broader city planning.

Spare Parts Management (SPM) and Industry 4.0 has proven their importance. However, employment of Industry 4.0 solutions for SPM is at emerging stage. To address the issue, this article is aimed toward a systematic literature review on SPM in Industry 4.0 era and identification of research gaps in the field with prospects.

Research articles were reviewed and analyzed through a content-based analysis using four step process model. The proposed framework consists of five categories such as Inventory Management, Types of Spares, Circularity based on 6Rs, Performance Indicators and Strategic and Operational. Based on these categories, a total of 118 research articles published between 1998 and 2022 were reviewed.

The technological solutions of Industry 4.0 concepts have provided numerous opportunities for SPM. Industry 4.0 hi-tech solutions can enhance agility, operational efficiency, quality of product and service, customer satisfaction, sustainability and profitability.

The review of articles provides an integrated framework which recognizes implementation issues and challenges in the field. The proposed framework will support academia and practitioners toward implementation of technological solutions of Industry 4.0 in SPM. Implementation of Industry 4.0 in SPM may help in improving the triple bottom line aspect of sustainability which can make significant contribution to academia, practitioners and society.

The examination uncovered a scarcity of research in the intersection of SPM and Industry 4.0 concepts, suggesting a significant opportunity for additional investigative efforts.

As rural nonfarm activities grow in developing countries, less attention is being paid to the opportunities they may provide for women. The purpose of this study is to examine the gender-differentiated impact of nonfarm diversification strategies in rural Senegal.

This study uses data collected from the Senegalese poverty monitoring survey and employs an instrumental variable (IV) approach and a multinomial endogenous treatment model to investigate the extent to which diversification strategies lead to improved outcomes for rural women and their households.

While nonfarm diversification is a male-dominated livelihood strategy, rural women make the most of it, regardless of whether they diversify into low- or high-return nonfarm activities. At the individual level, diversification improves rural women’s well-being through large income-increasing effects and higher empowerment but has no effect on rural men’s well-being. At the household level, the authors find that, when only women diversify, households have lower per capita income but are less likely to be food insecure than when only men or both genders diversify.

This study is based on cross-sectional data, making it impossible to examine the dynamic effects of nonfarm diversification strategies on well-being outcomes.

This study contributes to the current literature on rural livelihood diversification. While much attention has been paid to the feminization of agriculture, remarkably little is known about the expanding role of rural women in the nonfarm sector.

In the hybrid knowledge-sharing platform where paid and nonpaid (“free”) knowledge activities coexist, users’ free knowledge contribution may be influenced by financial factors. From the perspective of opportunity cost, this study investigates the direct effect of how the amount of monetary income from users’ contribution to paid knowledge activities influences their free knowledge contribution behavior in the future. Further, this study aims to verify the interaction effect of financial and nonfinancial factors (i.e. the experience of free knowledge contribution and social recognition) on free knowledge contribution.

Objective data was collected from a hybrid knowledge-sharing platform in China and then analyzed by using zero-inflated negative binomial regression model.

Results show that the amount of monetary income that knowledge suppliers gain from paid knowledge contribution negatively influences their free knowledge contribution. Experience of free knowledge contribution strengthens the negatively main effect, while social recognition has the weakening moderating role.

Although some studies have explored and verified the positive spillover effect of financial incentives on free knowledge contribution, the quantity dimension is ignored. This study examines the hindering influence of the quantity of monetary income from the perspective of opportunity cost. By taking the characteristic of knowledge suppliers and platforms as moderators, this study deepens the understanding of the influence of monetary income on free knowledge contribution in the hybrid knowledge-sharing platform.

The booming social media attracts construction professionals (CPs) to express emotions caused by work pressure (WP) through online behaviors. Previous works focus on the analysis of WP and emotions but do not adequately consider how WP can be reflected through online emotions. Thus, this study aims to attempt to explore the quantitative relationship between online emotional intensity and WP.

This study developed a linguistic-sticker (LS) model to quantitatively evaluate the sentiment intensity of posts published on social media. Moreover, the authors designed two econometric models of ordinary least squares regression and negative binomial regression to test the hypothesis.

The research found that posts with stronger negative sentiment (or positive sentiment) indicate that CPs face higher (or lower) WP. Besides, there is a negative bias between the sentiment intensity of posts and the comment quantity.

The positive correlation between sentiment intensity of posts and WP has been confirmed, which indicates that construction managers should pay more attention to CPs' behavior on social media, and take a more direct way to analyze work-related online behavior (e.g. posting, commenting). The dynamic monitoring of emotion-related posts also provides a direct basis for the management team to learn about CP's pressure status and propose measures to reduce their negative emotions. Furthermore, the emotional posts published by CPs on social media provide a direct basis for team managers to obtain their psychological state.

The research contributes to incorporating CPs' emotions into the LS model and to providing information systems artifacts and new findings on the analysis of WP and online emotions.

This paper aims to explore the significance of resource sharing in business to capture new market opportunities and securing competitive advantages. Firms enter strategic alliances (SAs), especially for designing new products and to overcome challenges in today’s fast changing environment. Research projects have dealt with the creation of SAs, however without concrete referencing the impact on selected supply chain resources. Furthermore, academia rather focused on elaborating the advantages and disadvantages of SAs and how this affects structural changes in the organization than examining the effects on supply chain complexity and performance.

The authors collected and triangulated a multi-industry data set containing primary data coming from more than 200 experts in the field of supply chain management along and secondary data coming from Refinitiv’s joint ventures (JVs) and SA database and IR solutions’ database for annual reports. The data is evaluated in three empirical settings using binomial testing and structural equation modeling.

The results show that nonequity SAs and JVs have varying degrees of impact on supply chain resources due to differences in the scope of the partnership. This has a negative impact on the complexity of the supply chain, with the creation of a JV leading to greater complexity than the creation of a nonequity SA. Furthermore, the findings prove that complexity negatively impacts overall supply chain performance. In addition, this study elaborates that increased management capabilities are needed to exploit the potentials of SAs and sheds light on hurdles that must be overcome within the supply network when forming a partnership. Finally, the authors give practical implications on how organizations can cope with increasing complexity to lower the risk of poor supply chain performance.

This study investigates occurring challenges when establishing nonequity SAs or JVs and how this affects their supply chain by examining supply networks in terms of complexity and performance.

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.

Knowledge sharing in online health communities (OHCs) disrupts consumers' health information-seeking behavior patterns such as seeking health information and consulting. Based on social exchange theory, this study explores how the two dimensions of experts' free knowledge sharing (general and specific) affect customer transactional and nontransactional engagement behavior and how the quality of experts' free knowledge sharing moderates the above relationships.

We adopted negative binomial regression models using homepage data of 2,982 experts crawled from Haodf.com using Python.

The results show that experts' free general knowledge sharing and free specific knowledge sharing positively facilitate both transactional and nontransactional engagement of consumers. The results also demonstrate that experts' efforts in knowledge-sharing quality weaken the positive effect of their knowledge-sharing quantity on customer engagement.

This study provides new insights into the importance of experts' free knowledge sharing in OHCs. This study also revealed a “trade-off” between experts' knowledge-sharing quality and quantity. These findings could help OHCs managers optimize knowledge-sharing recommendation mechanisms to encourage experts to share more health knowledge voluntarily and improve the efficiency of healthcare information dissemination to promote customer engagement.

Current open innovation (OI) and external knowledge search (EKS) research primarily shows a positive linear relationship between EKS and innovation at an individual level. However, organizational scholarship argues that excessive EKS may harm innovation. This study combines the knowledge-based view (KBV) and attention-based view (ABV) to articulate a nonlinear theory of EKS and innovation at the individual level.

The authors constructed a multi-sourced dataset covering 59,798 USA pharmaceutical patents spanning from 1975 to 2014 and employed negative binomial fixed-effect models to examine theoretical hypotheses.

We find a significant concave curvilinear relationship between EKS and innovation quantity as well as innovation quality at an individual level. An individual’s knowledge breadth and depth moderate the relationship between EKS and innovation, such that the threshold at which EKS has diminishing returns for individual innovation is higher for inventors with a broad range of knowledge and those with deeper expertise in the domain where they are innovating.

Managers should guide inventors toward a moderate investment of time and effort in EKS and should caution against over searching. Besides, managers should recognize that an inventor’s capacity for EKS is determined in part by their breadth of knowledge across various domains as well as the depth of knowledge they have in the knowledge domain where they are innovating.

We provide both parties with a clearer understanding of when EKS can begin to deteriorate an individual’s innovation performance why that deterioration occurs, and we also highlight two individual-level knowledge characteristics to take into consideration when deciding when to cease the EKS process.

This study provides a novel holistic understanding of OI and knowledge management for policymakers and organizations to nourish innovation dynamism and make the best of knowledge stocks in the community, which in turn will create endless power for sustainable social change and inclusive development.

This study contributes to OI theory by highlighting the non-linear nature of the relationship between EKS and innovation on an individual level. This represents a fundamental shift in theory on EKS and individual innovation by suggesting a major rethinking of how the two concepts relate, revealing the dark side of EKS in knowledge management if inventors engage in excessive EKS. Likewise, our study’s incorporation of the ABV informs KBV scholarship by highlighting the role of the limited attentional capacity of individuals in firm knowledge management.