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To clarify divergent conclusions on the impact of alliances on green innovation (GI), this study aims to examine the non-linear relationships between dual alliance and GI, as well as the mediation of green knowledge reconstruction (GKR) and the moderation of alliance tie strength.

Based on the theory of knowledge-based view, a moderated intermediary model is constructed by introducing GKR and alliance tie strength. The hypotheses are validated by using hierarchical regression analysis and bootstrapping method, with questionnaire survey data collected from 316 manufacturing firms in China.

Empirical results show that both exploratory alliance and exploitative alliance have an inverted U-shaped effect on GI, in which GKR plays a mediating role in the above relationship. Moreover, alliance tie strength weakens the intermediary role of GKR in the relationship between exploratory alliance and GI, whereas it enhances the intermediary role of GKR in the relationship between exploitative alliance and GI.

Findings reveal the non-linear effects of dual alliance on GI and clarify the inconsistent conclusions by proposing the moderated intermediary effect model. Moreover, this research reveals the mechanism of dual alliance on GI through the mediation of GKR and enriches the boundary conditions by integrating the moderating role of alliance tie strength.

In this study we investigated the effects of news reports on acquirer short-term performance. Our focus was on the extent to which key deal characteristics – the type of deal, during a merger wave or not or the presence of a significant premium – are made explicit. Moreover, we looked for the effect of the assessment of the deal characteristics by different key informants: board members, top management team members, and analysts. Configurations derived using the set-theoretic approach suggest that media-transmitted signals form complex interrelations among content and informant. We found that investors react positively to deals that are surrounded by unequivocal signals of synergy potential: they contain explicitly stated deal characteristics as well as deal endorsements from the boards and/or top management of acquirer and target companies. Analysts’ assessments of the deals seem to bear little influence on investor reaction. Meanwhile, investors react negatively to deals with low or absent media coverage as well as deals surrounded by signals of ambiguous synergy potential.

The outdoor event market was devastated during the COVID-19 pandemic because of social distancing measures. Therefore, this study aimed to explore stereotyped tendencies and behavioral intentions associated with the prosocial and sustainable practices of outdoor event participants to assess shifts in industry paradigms.

This study adopted structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to relatively examine sequential and combined effects of cognitive (knowledge of COVID-19, awareness of consequences, ascribed responsibility and perceived threat of COVID-19), affective (positive and negative anticipated emotions) and normative drivers (social and moral norms) on intention to practice social distancing requirements. The impact of cultural differences was further explored by comparing attendees from China and USA.

The SEM results showed that most cognitive drivers significantly affected affective drivers and normative drivers, leading to the intention to practice social distancing requirements. In addition, China and the USA showed significant differences on six paths including the path from moral norm to intention to practice social distancing requirements. Further, fsQCA results revealed the important combination of the factors that affects social distancing intention.

This study provides meaningful theoretical and practical implications for outdoor events scholars and managers. The research suggests a changing direction in event studies and shares ideas on how to manage and make outdoor events a new success after the pandemic.

This is the first study to adopt a mixed method of SEM and fsQCA attempt to explore the driving forces of outdoor participants’ pro-social behavior from cognitive, affective and normative perspectives.

The purpose of this paper is to propose a new approach to retrieve similar questions in online health communities to improve the efficiency of health information retrieval and sharing.

This paper proposes a hybrid approach to combining domain knowledge similarity and topic similarity to retrieve similar questions in online health communities. The domain knowledge similarity can evaluate the domain distance between different questions. And the topic similarity measures questions’ relationship base on the extracted latent topics.

The experiment results show that the proposed method outperforms the baseline methods.

This method conquers the problem of word mismatch and considers the named entities included in questions, which most of existing studies did not.

The aero-engine array electrostatic monitoring technology (AEMT) can provide more and more accurate information about the direct product of the fault, and it is a novel condition monitoring technology that is expected to solve the problem of high false alarm rate of traditional electrostatic monitoring technology. However, aliasing of the array electrostatic signals often occurs, which will greatly affect the accuracy of the information identified by using the electrostatic sensor array. The purpose of this paper is to propose special solutions to the above problems.

In this paper, a method for de-aliasing of array electrostatic signals based on compressive sensing principle is proposed by taking advantage of the sparsity of the distribution of multiple pulse signals that originally constitute aliased signals in the time domain.

The proposed method is verified by finite element simulation experiments. The simulation experiments show that the proposed method can recover the original pulse signal with an accuracy of 96.0%; when the number of pulse signals does not exceed 5, the proposed method can recover the pulse peak with an average absolute error of less than 5.5%; and the recovered aliased signal time-domain waveform is very similar to the original aliased signal time-domain waveform, indicating that the proposed method is accurate.

The proposed method is one of the key technologies of AEMT.

The purpose of this study was to advance the knowledge of pharmaceutical supply chain (PSC) resilience using complex adaptive system theory (CAS).

An exploratory research design, which adopted a qualitative approach was used to achieve the study’s research objective. Qualitative data were gathered through 23 semi-structured interviews with key supply chain actors across the PSC in the UK.

The findings demonstrate that CAS, as a theory, provides a systemic approach to understanding PSC resilience by taking into consideration the various elements (environment, PSC characteristics, vulnerabilities and resilience strategies) that make up the entire system. It also provides explanations for key findings, such as the impact of power, conflict and complexity in the PSC, which are influenced by the interactions between supply chain actors and as such increase its susceptibility to the negative impact of disruption. Furthermore, the antecedents for building resilience strategies were the outcome of the decision-making process referred to as co-evolution from a CAS perspective.

Based on the data collected, the study was able to reflect on the relationships, interactions and interfaces between actors in the PSC using the CAS theory, which supports the proposition that resilience strategies can be adopted by supply chain actors to enhance this service supply chain. This is a novel empirical study of resilience across multiple levels of the PSC and as such adds valuable new knowledge about the phenomenon and the use of CAS theory as a vehicle for exploration and knowledge construction in other supply chains.

Stock price prediction is a hot topic and traditional prediction methods are usually based on statistical and econometric models. However, these models are difficult to deal with nonstationary time series data. With the rapid development of the internet and the increasing popularity of social media, online news and comments often reflect investors’ emotions and attitudes toward stocks, which contains a lot of important information for predicting stock price. This paper aims to develop a stock price prediction method by taking full advantage of social media data.

This study proposes a new prediction method based on deep learning technology, which integrates traditional stock financial index variables and social media text features as inputs of the prediction model. This study uses Doc2Vec to build long text feature vectors from social media and then reduce the dimensions of the text feature vectors by stacked auto-encoder to balance the dimensions between text feature variables and stock financial index variables. Meanwhile, based on wavelet transform, the time series data of stock price is decomposed to eliminate the random noise caused by stock market fluctuation. Finally, this study uses long short-term memory model to predict the stock price.

The experiment results show that the method performs better than all three benchmark models in all kinds of evaluation indicators and can effectively predict stock price.

In this paper, this study proposes a new stock price prediction model that incorporates traditional financial features and social media text features which are derived from social media based on deep learning technology.

As the role of AI on human teams shifts from a tool to a teammate, the implementation of AI teammates into knowledge-intensive crowdsourcing (KI-C) contest teams represents a forward-thinking and feasible solution to improve team performance. Since contest teams are characterized by virtuality, temporality, competitiveness, and skill diversity, the human-AI interaction mechanism underlying conventional teams is no longer applicable. This study empirically analyzes the effects of AI teammate attributes on human team members’ willingness to adopt AI in crowdsourcing contests.

A questionnaire-based online experiment was designed to perform behavioral data collection. We obtained 206 valid anonymized samples from 28 provinces in China. The Ordinary Least Squares (OLS) model was used to test the proposed hypotheses.

We find that the transparency and explainability of AI teammates have mediating effects on human team members’ willingness to adopt AI through trust. Due to the different tendencies exhibited by members with regard to three types of cognitive load, nonlinear U-shaped relationships are observed among explainability, cognitive load, and willingness to adopt AI.

We provide design ideas for human-AI team mechanisms in KI-C scenarios, and rationally explain how the U-shaped relationship between AI explainability and cognitive load emerges.

Medicine shortages have a detrimental impact on stakeholders in the pharmaceutical supply chain (PSC). Existing studies suggest that building resilience strategies can mitigate the effects of these shortages. As such, this research aims to examine whether resilience strategies can reduce the impact of medicine shortages in the United Kingdom's (UK) PSC.

A sequential mixed-methods approach that involved qualitative and quantitative research enquiry was employed in this study. The data were collected using semi-structured interviews with 23 key UK PSC actors at the qualitative stage. During the quantitative phase, 106 respondents completed the survey questionnaires. The data were analysed using partial least square-structural equation modelling (PLS-SEM).

The results revealed that reactive and proactive elements of resilience strategies helped tackle medicine shortages. Reactive strategies increased relational issues such as behavioural uncertainty, whilst proactive strategies mitigated them.

The findings suggest that PSC managers and decision-makers can benefit from adopting structural flexibility and proactive strategies, which are cost-effective measures to tackle medicine shortages. Also engaging in strategic alliances as a proactive strategy mitigates relational issues that may arise in a complex supply chain (SC).

This study is the first to provide empirical evidence of the impact of resilience strategies in mitigating medicine shortages in the UK's PSC.