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Food markets are characterized by asymmetric information between suppliers and consumers, which causes inefficiency of market and food safety risks. This paper studies how the food quality and safety information disclosed by the government affects the hazard analysis and critical control point (HACCP) certification decision of meat producers. The heterogeneity of the effects across different regions, provinces with different meat output scales and provinces with different intensities of food safety regulation is evaluated.

This paper applies a unique database comprising information from multiple sources. Food quality and safety information disclosure is indicated by the number of failure records of food sampling inspections by the government in 2015–2018. Fixed-effect model is used in the analyses.

The results demonstrate that food quality and safety information disclosure has a significant effect on the HACCP certification adoption by meat producers. The effect is heterogeneous across geographic regions, i.e. this effect is larger in the east and the middle of China than that in the west and the northeast. The heterogeneity across regions may be caused by the variance in meat output scales and fiscal expenditures on food safety among provinces.

This research is one of the preliminary attempts to understand how producers respond in terms of HACCP certification to the amount of food quality and safety information disclosed by the government, based on the case of meat industry in China.

Limited geometric accuracy is one of the major challenges that hinder the wider application of additive manufacturing (AM). This paper aims to predict in-plane shape deviation for online inspection and compensation to prevent error accumulation and improve shape fidelity in AM.

A sequence-to-sequence model with an attention mechanism (Seq2Seq+Attention) is proposed and implemented to predict subsequent layers or the occluded toolpath deviations after the multiresolution alignment. A shape compensation plan can be performed for the large deviation predicted.

The proposed Seq2Seq+Attention model is able to provide consistent prediction accuracy. The compensation plan proposed based on the predicted deviation can significantly improve the printing fidelity for those layers detected with large deviations.

Based on the experiments conducted on the knee joint samples, the proposed method outperforms the other three machine learning methods for both subsequent layer and occluded toolpath deviation prediction.

This work fills a research gap for predicting in-plane deviation not only for subsequent layers but also for occluded paths due to the missing scanning measurements. It is also combined with the multiresolution alignment and change point detection to determine the necessity of a compensation plan with updated G-code.

The purpose of this paper is to examine the dividend payments and share repurchases of dual-class firms that have both their superior voting shares and inferior voting shares publicly traded.

This paper uses matched dual-class and single-class samples from 1994 to 2015 and logit models to evaluate the likelihoods of dividend payment and share repurchase between dual-class firms and single-class firms.

The results show that dual-class firms are more likely than the matched sample of single-class firms to pay dividends in both share classes. Dual-class firms, however, are more likely to repurchase their superior shares than single-class firms and their inferior shares.

The results suggest that dual-class firms do not use corporate payouts to either mitigate agency problems or maintain the private benefits of control. Instead, dual-class firms use dividend payments to mitigate agency problems while using repurchases of superior shares to maintain the private benefits of control, which supports the agency payout hypothesis.

This paper highlights the differences between dividend payments and share repurchases as forms of corporate payouts and suggests that firms may choose a particular form for a particular purpose.

This paper provides the first piece of empirical evidence on the corporate payouts of dual-class firms separating their superior voting shares and inferior voting shares.

Additive manufacturing (AM) has shown its capability in producing complex geometries. Due to the additive nature, the in situ layer-wise inspection of geometric accuracy is essential to making AM reach its full potential. This paper aims to propose a novel automated in-plane alignment and error quantification framework to distinguish the fabrication, measurement and alignment errors in AM.

In this work, a multi-resolution framework based on wavelet decomposition is proposed to automatically align two-dimensional point clouds via a polar coordinate representation and then to differentiate errors from different sources based on a randomized complete block design approach. In addition, a two-stage optimization model is proposed to find the best configuration of the multi-resolution framework.

The proposed framework can not only distinguish errors attributed to different sources but also evaluate the performance and consistency of alignment results under different levels of details.

A sample part with different featured layers, including a simple free-form layer, a defective layer and a layer with internal features, is used to illustrate the effectiveness and efficiency of the proposed framework. The proposed alignment method outperforms the widely used iterative closest point algorithm.

This work fills a research gap of state-of-the-art studies by automatically quantifying different types of error inherent in manufacturing, measuring and part alignment.

This study examines the heterogeneous correlations between rural farmers' land renting behavior and their grain production when they experienced a significant price decline.

We used well-timed panel data obtained from a two-round survey held in 2013 and 2017 among 621 households in the North China Plain. The empirical analyses were conducted by using the pooled ordinary least squares (OLS) and fixed effects models.

Rural tenants were having heterogeneous responses in land renting behavior and agricultural production when there was a price decline. A group of optimistic tenants (as professional farmers) were more likely to enlarge the farm scale for grain production through land rental markets but decrease variable investment levels (and subsequently decreased productivity) to cope with price decline. In contrast, nonprofessional farmers (the other rural tenants) were rather pessimistic about market performance, and they significantly decreased their grain production area to cope the price decline, but there was no decrease in grain productivity through reducing variable inputs.

This study contributes to the extant literature on the relationship between farmers' land renting-in behavior and agricultural production. By dividing the tenants into professional and nonprofessional farmers, we argue that there is a significant heterogeneous correlation between rural tenants' land renting behavior and grain production when farmers experience a price decline.

Digital technology is crucial to improving a firm’s core competitiveness. However, the existing research on the relationship therein shows heterogeneity. Using digital technology can enhance competitive advantage, which is crucial for enterprises and scholars. Thus, based on the digital technology affordance theory, this study explores the relationship between digital technology affordance and digital competitive advantage.

Survey data were collected from 509 large and medium-sized manufacturing enterprises in China, and multiple regression and structural equation modelling were used to test the hypotheses. Specifically, we discuss the mediating role of digital business capability and the moderating role of organisational legitimacy.

Editability, association and visibility positively affect digital competitive advantage, and their coordination is strong. Further, they can help enterprises gain a competitive advantage through the mediating role of digital business capability (digital strategy, digital integration and regulation). However, the influence effect and action path differ per in different dimensions. Organisational legitimacy positively moderates the mediating effect of digital integration and regulation, and there is a moderated mediating effect. However, the moderating effect on the mediating effect of digital strategy is not significant.

Existing studies neglect the relationship between the coordination of digital technology functions and digital competitive advantage. This study provides a new theoretical explanation for an in-depth understanding of these issues. These findings promote the development of innovation theory and provide valuable insights for guiding the application of digital technology in enterprises.

Fierce competition in the crowdfunding market has resulted in high failure rates. Owing to their dedication and efforts, many founders have relaunched failed campaigns as a second attempt. Despite the need for a better understanding, the success of campaign relaunches has not been well-researched. To fill this research gap, this study first theorizes how founders’ learning may enhance their competencies and influence investors’ attribution of entrepreneurial failure. The study then empirically documents the extent and conditions under which such learning efforts impact campaign relaunch performance.

This study examines 5,798 Kickstarter-relaunched campaigns. The founders’ learning efforts are empirically captured by key changes in campaign design that deviate from past business practices. Word movers’ distances and perceptual hashing algorithms (pHash) are used separately to measure differences in campaign textual descriptions and pictorial designs.

Differences in textual descriptions and pictorial designs during campaign failure–relaunch are positively associated with campaign relaunch success. The impacts are further amplified when the previous failures are more severe.

This study is one of the first to examine the success of a campaign relaunch after an initial failure. This study contributes to a better understanding of founders’ learning in crowdfunding contexts and provides insights into the strategies founders can adopt to reap performance benefits.

Lean manufacturing has been pivotal in emphasizing the reduction of cycle times, minimizing manufacturing costs and diminishing inventories. This research endeavors to formulate a lean data management paradigm, through the design and execution of a strategic edge-cloud data governance approach. This study aims to discern anomalous or unforeseen patterns within data sets, enabling an efficacious examination of product shortcomings within manufacturing processes, while concurrently minimizing the redundancy associated with the storage, access and processing of nonvalue-adding data.

Adopting a lean data management framework within both edge and cloud computing contexts, this study ensures the preservation of significant time series sequences, while ascertaining the optimal quantity of normal time series data to retain. The efficacy of detected anomalous patterns, both at the edge and in the cloud, is assessed. A comparative analysis between traditional data management practices and the strategic edge-cloud data governance approach facilitates an exploration into the equilibrium between anomaly detection and space conservation in cloud environments for aggregated data analysis.

Evaluation of the proposed framework through a real-world inspection case study revealed its capability to navigate alternative strategies for harmonizing anomaly detection with data storage efficiency in cloud-based analysis. Contrary to the conventional belief that retaining comprehensive data in the cloud maximizes anomaly detection rates, our findings suggest that a strategic edge-cloud data governance model, which retains a specific subset of normal data, can achieve comparable or superior accuracy with less normal data relative to traditional methods. This approach further demonstrates enhanced space efficiency and mitigates various forms of waste, including temporal delays, storage of noncontributory normal data, costs associated with the analysis of such data and excess data transmission.

By treating inspected normal data as nonvalue-added, this study probes the intricacies of maintaining an optimal balance of such data in light of anomaly detection performance from aggregated data sets. Our proposed methodology augments existing research by integrating a strategic edge-cloud data governance model within a lean data analytical framework, thereby ensuring the retention of adequate data for effective anomaly detection.

The purpose of this study was to investigate the differences in night eating status according to adolescents’ body mass index (BMI).

This was a cross-sectional study that included a total of 688 middle-school students. The subjects were categorized as underweight, normal weight and overweight according to their BMI, and their night eating intake patterns, night eating menu preference and intake frequency were compared and analyzed.

With regard to their night eating frequency, 39.8 per cent replied almost never, while 24.3 per cent replied once a week and less and 22.5 per cent replied two to three times a week. Among 11 night eating menus, the preference for fast foods, confectioneries, street foods and noodles was significantly higher in the underweight group than in the overweight group. The intake frequency of night eating menus such as fast foods, confectioneries and breads increased in the following order: overweight, normal and underweight group. The underweight group had a higher frequency of night eating, and they preferred to eat snacks more frequently from their night eating menu.

In conclusion, it is necessary to form positive dietary habits including nighttime eating for proper dietary management of adolescents.

A new and explicit form of the elastic strain-energy function for modeling large strain elastic responses of soft solids is constructed based on Hencky's logarithmic strain tensor.

Well-designed invariants of the Hencky strain are introduced for characterizing deformation modes and, furthermore, a new interpolating technique is proposed for combining piecewise splines into a single smooth function.

With this new form and this new technique, objectives in three respects may be achieved for the first time.

First, no adjustable parameters need to be treated. Second, large strain responses for three benchmark modes are derivable in a decoupled sense without involving strongly nonlinear coupling effects. Finally, large strain data may be automatically and accurately matched for three benchmark modes, including uniaxial, equi-biaxial and plane-strain extension. Numerical examples are presented and compared with usual approaches.