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As a counterproductive interpersonal work behavior, knowledge hiding inhibits team creativity, hampers collaboration and ultimately has a detrimental impact on organizational performance. Drawing upon the impression management perspective. This study aims to investigate how and when employees’ political skill affects their knowledge-hiding behavior in real work contexts.

The authors tested the hypotheses using data gathered from 266 employees in China using a time-lagged research design.

The results indicate that political skill positively influences knowledge hiding through the supplication strategy. Moreover, the positive effect of political skill on this strategy is stronger under higher levels of competition.

A cross-sectional design and the use of self-report questionnaires are the limitations of this study.

The authors contribute to the literature on the emergence of knowledge hiding by identifying an impression management perspective. The authors also contribute to the literature on political skill by exploring the potential negative effects of political skill in the interpersonal interaction. Moreover, the authors enrich the understanding of the literature in competitive climate by introducing the impression management theory and exploring its influence on knowledge floating.

Researchers continue to address the concept of self-disclosure because it is foundational for helping social networking sites (SNS) function and thrive. Nevertheless, the authors' literature review indicates that uncertainty remains around the underlying mechanisms and factors involved in the self-disclosure process. The purpose of this research is to better understand the self-disclosure process from the lens of dual-process theory (DPT). The authors consider both the controlled factors (i.e. self-presentation and reciprocity) and an automatic factor (i.e. social influence to use an SNS) involved in self-disclosure and broaden The authors proposed a model to include the interactive facets of enjoyment.

The proposed model was empirically validated by conducting a survey among users of WeChat Moments in China.

As hypothesized, this research confirms that enjoyment and automatic processing (i.e. social influence to use an SNS) are complementary in the SNS self-disclosure process and enjoyment negatively moderates the positive relationship between controlled factor (i.e. self-presentation) and self-disclosure.

Theoretically, this study offers a new perspective on explaining SNS self-disclosure by adopting DPT. Specifically, this study contributes to the extant SNS research by applying DPT to examine how the controlled factors and the automatic factor shape self-disclosure processes and how enjoyment influences vary across these processes – enriching knowledge about SNS self-disclosure behaviors. Practically, the authors provide important design guidelines to practitioners concerning devising mechanisms to foster more automatic-enjoyable value-added functions to improve SNS users' participation and engagement.

Based on the perspectives of social capital, image motivation and motivation affordances, this paper explores the direct and moderation effects of different kinds of motivations (i.e. relationship-based motivation, community-based motivation and individual-based motivation) on users' continuous knowledge contributions in social question and answer (Q&A) communities.

The authors collect the panel data of 10,193 users from a popular social Q&A community in China. Then, a negative binomial regression model is adopted to analyze the collected data.

The paper demonstrates that social learning, peer recognition and knowledge seeking positively affect users' continuous contribution behaviors. However, the results also show that social exposure has the opposite effect. In addition, self-presentation is found to moderate the influence of social factors on users' continuous use behaviors, while the moderation effect of motivation affordances has no significance.

First, this study develops a comprehensive motivation framework that helps gain deeper insights into the underlying mechanism of knowledge contribution in social Q&A communities. Second, this study conducts panel data analysis to capture the impacts of motivations over time, rather than intentions at a fixed time point. Third, the findings can help operators of social Q&A communities to optimize community norms and incentive mechanisms.

With the proliferation of ideas submitted by users in firm-built online user innovation communities, community managers are faced with the problem of user idea overload. The purpose of this paper is to explore the influencing factors on the idea adoption to identify high quality ideas, and then propose a method to quickly filter high value ideas.

The authors collected more than 110,000 data submitted by Xiaomi community users and analyzed the factors affecting idea adoption using a multinomial logistic regression model. In addition, the authors also used BP neural network to predict the idea adoption process.

The empirical results show that idea semantics, number of likes, number of comments, number of related posts, the existence of pictures and self-presentation have positive impact on idea adoption, while idea length and idea timeliness had negative impact on idea adoption. In addition, this paper calculates the idea evaluation value through the idea adoption process predicted by neural network and the mean value of idea term frequency inverse document frequency (TF-IDF).

This empirical study expands the theoretical perspective of idea adoption research by using dual-process theory and enriches the research methods in the field of idea adoption research through the multinomial logistic regression method. Based on our findings, firms can quickly identify valuable ideas and effectively alleviate the information overload problem of online user innovation communities.

This study aims at proposing a hybrid model for early cost prediction of a construction project. Early cost prediction for a construction project is the basic approach to procure a project within a predefined budget. However, most of the projects routinely face the impact of cost overruns. Furthermore, conventional and manual cost computing techniques are hectic, time-consuming and error-prone. To deal with such challenges, soft computing techniques such as artificial neural networks (ANNs), fuzzy logic and genetic algorithms are applied in construction management. Each technique has its own constraints not only in terms of efficiency but also in terms of feasibility, practicability, reliability and environmental impacts. However, appropriate combination of the techniques improves the model owing to their inherent nature.

This paper proposes a hybrid model by combining machine learning (ML) techniques with ANN to accurately predict the cost of pile foundations. The parameters contributing toward the cost of pile foundations were collected from five different projects in India. Out of 180 collected data entries, 176 entries were finally used after data cleaning. About 70% of the final data were used for building the model and the remaining 30% were used for validation.

The proposed model is capable of predicting the pile foundation costs with an accuracy of 97.42%.

Although various cost estimation techniques are available, appropriate use and combination of various ML techniques aid in improving the prediction accuracy. The proposed model will be a value addition to cost estimation of pile foundations.

This study aims to investigate the role of green strategic alliances (GSAs) in fostering a green shared vision (GSVis) and green shared value (GSV) and their impact on green organizational identity (GOI) and sustainability.

The authors employed structural equation modeling to analyze data collected through a 60-item questionnaire administered in Portugal and China, allowing the authors to test their theoretical model.

The findings of the authors' study indicate that green strategic alliances have a positive influence on the development of a GSVis and GSV in both countries. This, in turn, contributes to improved sustainability and the establishment of a GOI. Furthermore, the authors' results demonstrate that these alliances enhance GSV, resulting in enhanced sustainability performance and a stronger green identity, with a notable increase in awareness of environmental and social practices.

This article is innovative as it applies organizational learning and value creation theories to gain a deeper understanding of how alliances can shape the green identity of companies and contribute to their overall sustainability.

The existing literature on knowledge-sharing (KS) behavior in the organizational context demonstrates that there is diversity, if not divergence, in understanding KS. Thus, this paper aims to integrate social cognitive theory and social exchange theory to construct a research model for determining the incentive for knowledge sharing among individuals in organizations based on past empirical results.

Accordingly, the methodology adopted in this study is the meta-analytic structural equation modeling based on the data gathered from 78 studies (80 samples, n = 29,318).

The most significant predictors of KSB were organizational support and social interaction ties, whereby KS intention and attitude were most optimally predicted by organizational commitment, knowledge self-efficacy, social interaction ties, organizational expectancy and reciprocal benefit. This study carried out a moderation analysis to look into potential causes of inconsistent results.

This meta-analysis shows the most influencing factors that trigger KSB in organizations. Moreover, this study clarifies the possible reasons for the inconsistent findings of the previous studies. Thus, it contributes to the KS literature.

After the identification of a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at Wuhan, China, a pandemic was widely spread worldwide. In Italy, about 240,000 people were infected because of this virus including 34,721 deaths until the end of June 2020. To control this new pandemic, epidemiologists recommend the enforcement of serious mitigation measures like country lockdown, contact tracing or testing, social distancing and self-isolation.

This paper presents the most popular epidemic model of susceptible (S), exposed (E), infected (I) and recovered (R) collectively called SEIR to understand the virus spreading among the Italian population.

Developed SEIR model explains the infection growth across Italy and presents epidemic rates after and before country lockdown. The results demonstrated that follow-up of strict measures such that country lockdown along with high testing is making Italy practically a pandemic-free country.

These models largely help to estimate and understand how an infectious agent spreads in a particular country and how individual factors can affect the dynamics. Further studies like classical SEIR modeling can improve the quality of data and implementation of this modeling could represent a novelty of epidemic models.