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Prior research on green innovation has shown that institutional pressure stimulates enterprises to adopt green innovation. However, an institutional perspective does not explain why firms that face the same amount of institutional pressure execute different environmental practices and innovations. To address this research gap, the authors linked institutional theory with upper echelons theory and organization performance to build a comprehensive research model.

A total of 800 questionnaires were issued. The final usable questionnaires were 195, yielding a response rate of 24.38%. AMOS 23.0 was used to analyze the data and examine the relationships between the constructs in our model.

Institutional pressures affected both green innovation adoption (GIA) and the top management team's (TMT's) response. TMT's response influenced GIA. GIA was an important factor affecting firm performance. Furthermore, TMT's response mediated the relationship between institutional pressure and GIA. Institutional pressures indirectly affected green innovation performance but did not influence economic performance through GIA. Finally, TMT's response indirectly impacted firm performance through GIA.

The authors draw on institutional theory, upper echelons theory, and a performance-oriented perspective to explore the antecedents and consequences of GIA. This study has interesting implications for leaders and managers looking to implement green innovation and leverage it for firm performance to out compete with market rivals as well as to make the changes in collaboration with many other companies including market rivals to gain success in green innovation.

This paper aims to investigate the difference between the impacts of indicators of trade barriers (TBs) on bankrupt enterprises (BEs), new enterprises (NEs) and other enterprises (OEs).

The paper has used a multiple-step approach. At the first stage, the initial data has been collected from interviews with 164 top managers of SMEs in West Azerbaijan in Iran during two periods of 2013–2015 and 2017–2019. At the second step, multiple correspondence analysis has been used to summarize the relationships between variables and construct indices for different groups of TBs. Finally, the generalized structural equation model method was used to examine the impact of export barriers.

The results showed that the political legal index is the main TBs for BEs and NEs, but it had a more significant impact on BEs; the financial index was the second major TBs factor for BEs, while OEs did not have a problem in performance index, and the financial index was classified as a minor obstacle for them. All indicators of marketing barriers (except production index) had a negative and significant effect on all enterprises; the most important TBs for NEs was the information index.

The results indicated that if enterprises have a strong financial system and function, they can lessen the impact of sanctions and keep themselves in the market.

This study aimed to investigate how the enrichment of medical documents' index terms by their comments improves the relevance and novelty of the top-ranked results retrieved by an NLP system.

A semi-experimental pre-test and post-test research was designed to compare NLP-based indexes before and after being expanded by the comment terms. The experiments were conducted on a test collection of 13,957 documents commented by F1000-Prime reviewers. They were indexed at title, abstract, body and full-text levels. In total, 100 seed documents were randomly selected and served as queries. The textual similarity of the documents and queries was calculated using Lucene-more-like-this function and evaluated by the semantic similarity of their MeSH. The results novelty was measured using maximal marginal relevance and evaluated by their MeSH novelties. Normalized discounted cumulative gain was used to compare the basic and expanded indexes' precisions at 10, 20 and 50 top ranks.

The relevance and novelty of the results ranked at the top precision points was improved after expanding the indexes by the comment terms. The finding implies that meta-texts are effective in representing their mother documents, by adding dynamic elements to their rather static contents. It also provides further evidence about the merits of the application of social intelligence and collective wisdom reflected in the actions and reactions of users in tackling the challenges faced by NLP-based systems.

This is the first study to confirm that social comments on scientific papers improve the performance of information systems in terms of relevance and novelty.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2022-0283.

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.

Trust is the glue that holds cooperative relationships together and often exists in an asymmetric manner. The purpose of this study is to explore how to mitigate the issue of losses or increased transaction costs caused by opportunistic behavior in a soft environment where trust asymmetry is quite common and difficult to avoid.

This study focuses on examining asymmetric trust between the government and the private sector in public-private partnership (PPP) projects. Drawing upon both project realities and relevant literature, the primary conditional variables influencing asymmetric trust are identified. These variables encompass power perception asymmetry, information asymmetry, interaction behavior, risk perception differences and government-side control. Subsequently, through the use of a survey questionnaire, binary-matched data from both the government and the private sector are collected. The study employs fuzzy-set qualitative comparative analysis (fsQCA) to conduct a configurational analysis, aiming to investigate the causal pathways that trigger asymmetric trust.

No single conditional variable is a necessary condition for the emergence of trust asymmetry. The pathways leading to a high degree of trust asymmetry can be categorized into two types: those dominated by power perception and those involving a combination of multiple factors. Differences in power perception play a crucial role in the occurrence of high trust asymmetry, yet the influence of other conditional variables in triggering trust asymmetry should not be overlooked.

The findings can contribute to advancing the study of trust relationships in the field of Chinese PPP projects. Furthermore, they hold practical value in facilitating the enhancement of trust relationships between the government and the private sector.

Porosity is a commonly analyzed defect in the laser-based additive manufacturing processes owing to the enormous thermal gradient caused by repeated melting and solidification. Currently, the porosity estimation is limited to powder bed fusion. The porosity estimation needs to be explored in the laser melting deposition (LMD) process, particularly analytical models that provide cost- and time-effective solutions compared to finite element analysis. For this purpose, this study aims to formulate two mathematical models for deposited layer dimensions and corresponding porosity in the LMD process.

In this study, analytical models have been proposed. Initially, deposited layer dimensions, including layer height, width and depth, were calculated based on the operating parameters. These outputs were introduced in the second model to estimate the part porosity. The models were validated with experimental data for Ti6Al4V depositions on Ti6Al4V substrate. A calibration curve (CC) was also developed for Ti6Al4V material and characterized using X-ray computed tomography. The models were also validated with the experimental results adopted from literature. The validated models were linked with the deep neural network (DNN) for its training and testing using a total of 6,703 computations with 1,500 iterations. Here, laser power, laser scanning speed and powder feeding rate were selected inputs, whereas porosity was set as an output.

The computations indicate that owing to the simultaneous inclusion of powder particulates, the powder elements use a substantial percentage of the laser beam energy for their melting, resulting in laser beam energy attenuation and reducing thermal value at the substrate. The primary operating parameters are directly correlated with the number of layers and total height in CC. Through X-ray computed tomography analyses, the number of layers showed a straightforward correlation with mean sphericity, while a converse relation was identified with the number, mean volume and mean diameter of pores. DNN and analytical models showed 2%–3% and 7%–9% mean absolute deviations, respectively, compared to the experimental results.

This research provides a unique solution for LMD porosity estimation by linking the developed analytical computational models with artificial neural networking. The presented framework predicts the porosity in the LMD-ed parts efficiently.

Academic conferences play a crucial role in fostering scholarly exchanges and discussions among core members, thereby shaping the core performance of these events. Understanding the contributions of scholars in academic conferences is essential for advancing the goals and development of such events. This paper aims to discuss the aforementioned idea.

This paper proposes a comprehensive measurement method for measuring scholars' academic activeness in academic conferences. Based on the essence of academic activeness, a measurement model is constructed, comprising the breadth of academic activeness using the RFM model and the depth of academic activeness using the ATM model. Additionally, effective categories of scholars are derived from the model, facilitating the identification of valuable scholars in academic conferences.

Empirical evidence from ASIS&T conference records over the past 20 years demonstrates that the proposed measurement indicators have better coverage compared to traditional evaluation indicators, thus complementing existing scholar evaluation indictators. Furthermore, the academic activeness measurement model presented in this paper exhibits good efficacy in identifying important value and active scholars.

This paper pays attention to the activeness of scholars in academic conferences. It broadens the evaluation of academic conferences and scholars and supplements the evaluation indicators of academic influence.

Examining emotional solidarity that drives tourists’ nostalgia has received significant attention, offering valuable insights that can aid in the selection of a travel destination. However, tourists’ nostalgia, along with its antecedents—perceived safety risk and emotional solidarity, has gained less attention within the setting of Pakistan. Therefore, this research has been carried out and validated a research framework using the stimulus-organism-response model to investigate the links between perceived safety risk, emotional solidarity—welcoming nature, emotional closeness, sympathetic understanding, perceived nostalgia, and destination advocacy intentions. In addition, this model employed destination attachment—place identity and place dependence—as a boundary condition on the relationships between emotional solidarity and advocacy intentions.

This study collected 545 responses through two methods: offline distribution of print copies of the questionnaire and online surveys from domestic tourists who visited scenic destinations last year. Structural equation modeling (SEM) was performed using AMOS 26, and the PROCESS macro was conducted using IBM SPSS 28.

The findings highlighted that perceived safety risk negatively influences emotional solidarity—welcoming nature, emotional closeness, sympathetic understanding—, resulting in a positive effect on perceived nostalgia. Furthermore, tourists’ perceived nostalgia positively triggers advocacy intentions. The findings also confirmed the boundary conditions of destination attachment—place identity and place dependence—on the association between emotional solidarity and perceived nostalgia.

Three major practical implications of these findings: First, ensuring visitor safety with smart security measures, digital tools for quick response, and local community involvement. Second, highlighting local markets, art, and architecture to enrich cultural experiences and promote accessibility and diversity. Finally, using marketing to generate nostalgic experiences through local collaborations, professional storytelling, and engaging social media content to build emotional ties and curiosity.

In terms of originality, this is pioneering research intended at developing and validating the model in the context of Pakistani destinations. Furthermore, this marks the initial step in examining the proposed relationships between perceived safety risk and emotional solidarity in fostering tourists' perceived nostalgia, ultimately leading to a strong desire to advocate for the destination.

This paper aims to investigate the spillover effects of jump and crash events among Chinese nonfinancial firms.

This sample consists of more than 1.5 million weekly observations of over 3,000 Chinese listed firms over the period 1991–2015. The authors utilize univariate tests to compare the post-event performance of matched peer and non-peer control firms and cross-sectional regressions of their abnormal returns/cumulative abnormal returns (ARs/CARs) and returns on assets (ROAs).

The authors find that extreme risk-adjusted abnormal stock returns (stock price crashes and jumps) generate statistically significant ARs/CARs in the same directions in industry, size, leverage, and geographical location matched peer firms in Chinese stock market. Further tests reveal that peer firms' response to the crash event is pronounced more in the group of firms about which the information asymmetry is high between investors and firms.

Portfolio investors can adjust their portfolios accordingly by selling stocks of the matching rival firms during a crash period. Policymakers may develop policies so as to protect the interests of small investors in the events of crashes in the markets. They can reduce the information asymmetry between the firms and the investors by making information about the firms more transparent, so as to reduce the contagion in case of crash event.

This study has important implications for portfolio investment managers and policymakers.

To the best of authors' knowledge, this is the first study that combines the jump and crash events and attempts to assess their spillover effects on other firms in Chinese stock market.

This study aims to use “both–and” thinking of Yin-Yang philosophy to extend the field of leadership literatures and explore the influences of paradoxical leadership and emotional intelligence on organizational identification and turnover intentions of nurses.

The authors adopted a cross-sectional survey completed by 285 nurses in Taiwan. SPSS 22, PROCESS and AMOS 21 were used for data analysis.

The results reveal that paradoxical leadership has a significant positive relationship with nurses’ organizational identification and a significant negatively relationship with their turnover intentions, and organizational identification partially mediated the relationship between paradoxical leadership and turnover intentions. The results further show that emotional intelligence strengthens the effect of paradoxical leadership on organizational identification, and paradoxical leadership had a stronger indirect effect on turnover intentions through organizational identification under strong emotional intelligence.

Paradoxical leadership can strengthen managers’ abilities in dealing with interrelated and substantial issues and correspond to organizing and belonging paradoxes in holistic thinking processes. Health-care organizations must shape a coordinated institution and offer training initiatives to increase managers’ ability and attitude to control organizational rules and procedures while allowing employees’ flexibility and autonomy according to the requirements of the situation, which will maintain both organizational short-term benefits and long-term growth.