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The purpose of this paper is to investigate the effect of share pledging by controlling shareholders on earnings informativeness.

Using a sample of 23,120 firm-year observations from 2003 to 2019 in China, this paper examines how share pledging by controlling shareholders affects earnings informativeness, measured by earnings persistence and earnings response coefficients.

This paper finds that share pledging by controlling shareholders makes earnings less informative. The adverse impacts are more pronounced when share pledging distorts incentives of controlling shareholders to a greater extent and when the signaling of share pledging about a firm’s weak future performance is stronger. Finally, this paper further shows that the 2018 new regulation on share pledging effectively alleviates the negative impacts of share pledging on earnings informativeness.

First, this paper adds to the growing literature on the economic consequence of share pledging by documenting the adverse impacts of share pledging on earnings informativeness. The literature on the economic consequence of share pledging is often mixed, which justifies further research on the impacts of share pledging on earnings informativeness. Second, this paper documents a new signaling channel through which share pledging affects earnings informativeness. Third, the finding of this paper on the 2018 new regulation on share pledging may be interesting to research agencies, such as the Chartered Financial analyst institute and Institutional Shareholder Services institute that recommend tightening regulations on share pledging.

Literature concerning the linkages between entrepreneurial orientation (EO) and firm performance (FP) has been growing in tourism research. However, the linkage's relevance to new venture travel intermediaries remains vague. This study proposes a model that helps researchers and practitioners understand how EO translates into new venture FP through two strategic perspectives of value creation, i.e. firm value (FV) and customer perceived value (CPV).

The study tests this framework using structural equation modeling on a matched dyadic sample of 127 new venture firms belonging to the Indian travel industry.

The results posit that FV and CPV partially mediate the relationship between EO and new venture FP. The study advances the existing knowledge on the link between EO and FP and provides insights into how EO can enhance FV and CPV which ultimately enhances FP.

This work is the first to extend and integrate the idea of FV and CPV to entrepreneurship and new venture performance literature. By considering the two strategic aspects of value creation, i.e. FV and CPV, the paper presents a holistic view of value creation through EO.

The current research aimed to investigate the external enablement role of Digital Infrastructures (DI) in the interplay of entrepreneurial cognitions and innovation.

Data from the Global Entrepreneurship Monitor (GEM) and Digital Economy and Society Index (DESI) were used for analyses. This yielded a sample of 8,601 Generation Z entrepreneurs operating in 25 European countries.

Applying hierarchical moderated regressions showed that socio-cognitive components of an entrepreneurial mindset (self-efficacy, risk propensity, opportunity identification) affect innovation among Generation Z entrepreneurs. More importantly, DI plays an external enablement role in the interplay of cognitions and innovation among Generation Z entrepreneurs.

This study contributes to the socio-cognitive theory of entrepreneurship by integrating an external enablement perspective into the study of cognitions and entrepreneurial outcomes (here, innovation). It contributes to the digital technology perspective of entrepreneurship by connecting the conversation about the socio-cognitive perspective of entrepreneurship regarding the role of cognitions in innovation to the conversation in information systems (IS) regarding technology affordances and constraints. This study extends the application of the external enabler framework to the post-entry stage of entrepreneurial activity and integrates a generational perspective into it.

In this study, AlCoCrFeNi–Cu (Cu-based) and AlCoCrFeNi–Ti (Ti-based) high entropy alloys (HEAs) were fabricated using a direct blown powder technique via laser additive manufacturing on an A301 steel baseplate for aerospace applications. The purpose of this research is to investigate the electrical resistivity and oxidation behavior of the as-built copper (Cu)- and titanium (Ti)-based alloys and to understand the alloying effect, the HEAs core effects and the influence of laser parameters on the physical properties of the alloys.

The as-received AlCoCrFeNiCu and AlCoCrFeNiTi powders were used to fabricate HEA clads on an A301 steel baseplate preheated at 400°C using a 3 kW Rofin Sinar dY044 continuous-wave laser-deposition system fitted with a KUKA robotic arm. The deposits were sectioned using an electric cutting machine and prepared by standard metallographic methods to investigate the electrical and oxidation properties of the alloys.

The results showed that the laser power had the most influence on the physical properties of the alloys. The Ti-based alloy had better resistivity than the Cu-based alloy, whereas the Cu-based alloy had better oxidation residence than the Ti-based alloy which attributed to the compositional alloying effect (Cu, aluminum and nickel) and the orderliness of the lattice, which is significantly associated with the electron transportation; consequently, the more distorted the lattice, the easier the transportation of electrons and the better the properties of the HEAs.

It is evident from the studies that the composition of HEAs and the laser processing parameters are two significant factors that influence the physical properties of laser deposited HEAs for aerospace applications.

The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.

The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.

The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.

The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.

The Chinese housing market has gone through rapid growth during the past decade, and house price forecasting has evolved to be a significant issue that draws enormous attention from investors, policy makers and researchers. This study investigates neural networks for composite property price index forecasting from ten major Chinese cities for the period of July 2005–April 2021.

The goal is to build simple and accurate neural network models that contribute to pure technical forecasts of composite property prices. To facilitate the analysis, the authors consider different model settings across algorithms, delays, hidden neurons and data spitting ratios.

The authors arrive at a pretty simple neural network with six delays and three hidden neurons, which generates rather stable performance of average relative root mean square errors across the ten cities below 1% for the training, validation and testing phases.

Results here could be utilized on a standalone basis or combined with fundamental forecasts to help form perspectives of composite property price trends and conduct policy analysis.

This paper aims to report a non-randomized control study undertaken to investigate prevalence and correlates of conduct disorder among male secondary education students in South-West Nigeria and to assess the impact of a problem-solving skills and attributional retraining (PSSAR) intervention with this population.

In total, 787 male students from two schools were screened for conduct disorder. All participants who met criteria for the disorder were allocated to either treatment (n = 55) or control (n = 47) groups. Outcome measures comprised the strengths and difficulties questionnaire (SDQ; teacher and student versions) and the teacher rating of students’ aggressive behaviors.

Of the sample, 13% were found to present with difficulties that met criteria for conduct disorder. The presence of these difficulties correlated with several demographic variables, including parental conflict and alcohol use. A statistically significant reduction in mean scores was observed for the treatment group in the student rating of the SDQ emotional subscale and total difficulties scores. Teacher ratings were less consistent in that conduct problems, prosocial behavior and total difficulties increased following the intervention, whereas peer problems and aggressive behavior were reported by teachers to reduce. No statistically significant change was found in the outcome measures for the control group.

In resource-constrained settings, school-based interventions are an important means through which treatment gaps in child and adolescent mental health can be addressed.

In resource-constrained settings, school-based interventions are an important means through which treatment gaps in child and adolescent mental health can be addressed. This study’s findings offer some preliminary support for the PSSAR intervention for conduct disorder in this context and indicate areas for further research.

This paper aims to assess how digital knowledge management affects technostress in workers and how that influences the organization’s sustainability. The study applied an in-depth literature review of the following concepts and dimensions, digital knowledge management (acquisition, sharing and application), technostress (techno overload, invasion, complexity, insecurity and uncertainty) and sustainability (economic, social and environmental).

After completing the literature review, an online questionnaire was developed and disseminated through social networks. The questionnaire had four sections: classification of the respondent, questions related to knowledge management, technostress and sustainability. The final sample comprises 454 responses. First, a descriptive analysis of the sample was carried out, and second, a structural equation model by the partial least squares–structural equation modeling method was conducted.

The results show that there is a direct and positive relationship between knowledge management and technostress. This finding means that a higher level of knowledge management of firms causes a greater level of technostress among employees. The close relationship between knowledge management and the firms’ sustainability has also been confirmed. The study results have shown that gender does not have a moderating effect on the relationships reported, as there are no significant differences.

To the best of the authors’ knowledge, this study is the first to look at digital knowledge management, assessing both the levels of employee technostress and the sustainability achieved by their organizations. Thus, this study could serve as a basis for future research. In addition, it contributes to the scarce academic literature on technostress and digital knowledge management levels.

This paper aims to investigate the links between daily review sentiment and the hotel performance measures of occupancy rate (OR), average daily rate (ADR) and revenue per available room (RevPAR).

The authors conducted review sentiment analyses in three moments (−1, −7 and −14 days) before arrival time using a data set of budget hotel performance and online reviews. The aim was to identify the effect of review sentiment in the budget hotel market on the three performance metrics.

Daily sentiment positively affects ADR and negatively affects OR and RevPAR, but only up to a certain threshold, after which the trend reverses. Prices increase with the level of sentiment, and high prices lead to low OR and RevPAR only when the sentiment scores are low. When they are high, they are associated with low rates, which lead to high OR and RevPAR.

Daily review sentiment can be viewed as a valuable “barometer” indicating a hotel’s daily operational effectiveness. Daily sentiment can thus allow hotel managers to adjust their dynamic pricing strategies more accurately.

This study identifies daily sentiment as an alternative predictor of hotel performance. In addition to the roles of valence and volume in the decision-making process, the authors found that daily review sentiment can be an “in-the-moment” factor with a high impact, encouraging consumers to complete their transactions. This study suggests that aggregated measures such as the total number of reviews and overall ratings of the hotel should not be the sole consideration in reputation management.

Recycling and reuse of the electric vehicle (EV) batteries are ways to extend their limited lives. If batteries can be traced from production to recycling, it is beneficial for battery recycling and reuse. Using blockchain technology to build a smart EV battery reverse supply chain can solve the difficulties of lack of trust and data. The purpose of this study is to discuss the behavioural evolution of a smart EV battery reverse supply chain under government supervision.

This study adopts evolutionary game theory to examine the decision-making behaviours of the government, EV manufacturers with recycled used batteries and third-party EV battery recyclers lacking professional recycling qualification.

On the smart reverse supply chain integrated by blockchain technology, a cooperative recycling strategy of the third-party EV battery recycler is the optimal choice when the government tends to actively regulate. The probability of the EV manufacturer choosing the blockchain adoption strategy exceeds (below) the threshold, and the government prefers negative (positive) supervision. According to numerical analysis, in the mature stage in the EV battery recycling industry, when the investment cost of applying blockchain is high, EV manufacturers' willingness to apply blockchain slows down, the government accelerates adopting a negative supervision strategy and third-party EV battery recyclers prefer cooperative recycling.

The results of this study provide opinions on the strength of government supervision and the conditions under which EV manufacturers and third-party EV battery recyclers should apply blockchain and cooperate. On the other hand, this study provides theoretical analysis for promoting the application of blockchain technology in smart reverse supply chain.

Compared with previous research, this study reveals the relevance of government supervision, blockchain application and cooperation strategy in smart EV battery reverse supply chain. In the initial stage, even if the subsidy (subsidy reduction rate) and penalty are high and the penalty reduction rate is low, the EV manufacturer should rather give up the application of blockchain technology. In the middle stage in the EV battery recycling industry, the government can set a lower subsidy (subsidy reduction rate) combined with a penalty or a higher penalty (penalty reduction rate) combined with a subsidy to supervise it. The third-party EV battery recycler is advised to cooperate with the EV manufacturer when the subsidy is low or the penalty is high.