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The purpose of this study is to explore the motivational role of feedback information (positive and negative) provided by the firm in the face of participant heterogeneity, in terms of past success experience, under the research setting of crowdsourcing contests.

Taking insights from feedback studies and the dynamics of self-regulation theory, four theoretical hypotheses are proposed. An integrated dataset of 4,880 contest-participant pairs, which is obtained from an online contest platform and a survey, is empirically analyzed.

Empirical analysis shows that both positive feedback and negative feedback are able to stimulate the inner needs of participants. Notably, negative (positive) feedback becomes more (less) effective in intrinsically motivating crowds as they gain more successful experience during contest participation.

This study brings some new knowledge for the intrinsic motivation of crowds by exploring its antecedents, which have been undervalued in extant literature. The motivational role of feedback information is particularly explored.

Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.

The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.

A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.

The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.

This study aims to research the time-varying mesh stiffness (TVMS) model for orthogonal face gear drives considering elastohydrodynamic lubrication (EHL) and provide a theoretical basis for understanding the dynamic characteristics of face gear drives.

Considering EHL, a novel model is proposed to calculate the TVMS of orthogonal face gears using the deformation compatibility condition. First, the tooth surface equations of orthogonal face gears are derived according to the tooth surface generation principle. Then, the oil film thickness on the tooth surface of face gears is obtained by solving the governing equations of EHL. Furthermore, the proposed model is used to calculate the TVMS of face gears along the mesh cycle and is verified. Finally, the effects of module, tooth number of shaper cutter and pressure angle on mesh stiffness are analyzed.

The results indicate that when the contact ratio is greater than 1 and less than or equal to 2, the TVMS of face gears exhibits a phenomenon of double-single tooth alternating meshing where sudden changes occur. As the module increases, the overall mesh stiffness of face gears increases, and the magnitude of the sudden change at the moment of single-double tooth alternating meshing gradually increases. As the tooth number of shaper cutter and pressure angle increase, so does the TVMS of face gears. When the effect of oil film is considered, the calculated TVMS of face gears slightly increases overall and the increase in average oil film thickness leads to a rise in the TVMS. This study provides a theoretical basis for understanding the dynamic characteristics of face gear drives.

This study’s originality and value lie in its comprehensive approach, which includes conducting analysis based on loaded tooth contact, considering the influence of elastohydrodynamic lubrication, proposing a novel analytical–finite–element model, calculating TVMS of face gears, verifying the proposed model and analyzing the effects of typical structural parameters and oil film thickness.

This study aims to investigate the relationship between inventory flexibility, digital transformation, supply chain concentration, and productivity in the context of Chinese manufacturing enterprises.

Empirical analysis was conducted using data from listed Chinese manufacturing firms spanning from 2013 to 2022. The study employs a moderated model to examine how digital transformation influences the connection between inventory flexibility and productivity. Additionally, a moderated moderation model is utilized to explore the role of supply chain concentration in moderating the relationship among inventory flexibility, digital transformation, and productivity.

The study reveals a significant positive correlation between inventory flexibility and productivity, underlining the importance of flexible inventory management. Digital transformation moderates this relationship, with digital transformation enhancing the impact of inventory flexibility on productivity. Supplier and customer concentration also positively moderate this connection, suggesting a complementary relationship with digital transformation.

These findings offer valuable insights for managers and policymakers, emphasizing the need for a flexible approach to inventory management that considers the evolving digital landscape and supply chain dynamics.

This study contributes to the literature by providing empirical evidence of the nuanced relationship between inventory flexibility, digital transformation, supply chain concentration, and productivity in Chinese manufacturing enterprises. It underscores the importance of integrating digital transformation and supply chain concentration initiatives with flexible inventory management to optimize productivity in the business landscape.

New generation of construction workers (NGCWs) who were born in the 1980s and later have gradually become the main workforce of Chinese construction industry. They may behave differently when dealing with knowledge-related activities due to divergent characteristics caused by generational discrepancy. To provide a theoretical foundation for construction companies and safety managers to improve safety management, this research explores the factors and paths impacting the NGCWs' ability to share their safety knowledge.

Based on literature review, main factors that influence the safety knowledge sharing of the NGCWs were identified. Decision-Making Trial and Evaluation Laboratory and Interpretive Structural Modeling were applied to identify the hierarchical and contextual relations among the factors influencing the safety knowledge sharing of the NGCWs.

The results showed that sharing atmosphere ranked first in centrality and had a high degree of influence and being influenced, indicating itself an extremely important influencing factor of safety knowledge sharing of NGCWs. Six root influencing factors were identified, including individual characteristics, work pressure, sharing platform, incentive mechanism, leadership support and safety management system.

The number of influencing factors of safety knowledge sharing of the NGCWs identified in this study is limited, and the data obtained by the expert scoring method is subjective. In future studies, the model should be further developed and validated by incorporating experts from different fields to improve its integrity and applicability.

The influencing factors identified in this paper can provide a basis for construction companies and safety managers to improve productivity and safety management by taking relevant measures to promote safety knowledge sharing. The research contributes to the understanding knowledge management in the context of the emerging market. It helps to answer the question of how the market can maintain the economic growth success through effective knowledge management.

This paper investigates the influencing factors of NGCWs' safety knowledge sharing from the perspective of intergenerational differences, and the 13 influencing factor index system established expands the scope of research on factors influencing safety knowledge sharing among construction workers and fills the gap in safety knowledge sharing research on young construction workers. Furthermore, this paper establishes a multi-layer recursive structure model to clarify the influence path of the influencing factors and contributes to the understanding of safety knowledge sharing mechanism.

The aim of the study has been performed to investigate the relationship between sustainable supply chain management (SSCM) practices and the cost performance of manufacturing firms in Bangladesh. Moreover, this paper highlights the key environment-friendly approaches and their association with financial performance in Bangladesh.

The paper empirically assesses sustainable supply chain performance using four major supply chain practices, including sustainable procurement, sustainable production, sustainable distribution and investment recovery, and compares it with the cost performance. Twenty-four variables were identified through different literature and distributed as a structured questionnaire among the managers appointed in different manufacturing firms in Bangladesh. An empirical study was conducted using the Partial Least Square-Structural Equation Modeling (PLS-SEM) technique to examine the hypothesized relationships.

The results find a positive relationship in two variables of sustainable supply chain practices, including sustainable procurement and investment recovery, while sustainable distribution negatively impacted cost performance. In addition, sustainable production found no effect on cost performance.

The paper emphasizes the financial perspective of a sustainable supply chain without explicit consideration of sustainability's environmental and social dimensions.

This study has implications for the literature on the SSCM approaches of manufacturing firms in the least developed economies. In addition, this study could work as a guideline for some manufacturing industries that prefer a policy or standard to alter their traditional supply chain management system to a sustainable supply chain.

The paper provides a comprehensive framework for evaluating the coordinated effect of SSCM practices on cost performance where variables of four specific activities of SSCM and cost performance are adopted from different studies. Further studies could be initiated, including some other eco-friendly supply chain variables, and the effect could be evaluated from an environmental perspective.

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.

As supply chain excellence matters, designing an appropriate health-care supply chain is a great consideration to the health-care providers worldwide. Therefore, the purpose of this paper is to benchmark several potential health-care supply chains to design an efficient and effective one in the presence of mixed data.

To achieve this objective, this research illustrates a hybrid algorithm based on data envelopment analysis (DEA) and goal programming (GP) for designing real-world health-care supply chains with mixed data. A DEA model along with a data aggregation is suggested to evaluate the performance of several potential configurations of the health-care supply chains. As part of the proposed approach, a GP model is conducted for dimensioning the supply chains under assessment by finding the level of the original variables (inputs and outputs) that characterize these supply chains.

This paper presents an algorithm for modeling health-care supply chains exclusively designed to handle crisp and interval data simultaneously.

The outcome of this study will assist the health-care decision-makers in comparing their supply chains against peers and dimensioning their resources to achieve a given level of productions.

A real application to design a real-life pharmaceutical supply chain for the public ministry of health in Morocco is given to support the usefulness of the proposed algorithm.

The novelty of this paper comes from the development of a hybrid approach based on DEA and GP to design an appropriate real-life health-care supply chain in the presence of mixed data. This approach definitely contributes to assist health-care decision-makers design an efficient and effective supply chain in today’s competitive word.

This study aims to enhance the understanding of the impact of the COVID-19 pandemic on corporate tax performance in the context of a large emerging country like Indonesia.

This study uses a quantitative approach with multiple regression methods on a data set of 2,366 firm-year observations registered on the Indonesia Stock Exchange (IDX) from 2017 to 2022.

The primary empirical findings from the multivariate regressions suggest a positive and significant association between the COVID-19 pandemic and corporate tax performance in Indonesia. In other words, these listed firms have increased their tax avoidance activities during the pandemic. As firms face financial hardships due to the pandemic's effects, they tend to engage in tax avoidance practices to reduce current income tax payments, thereby enhancing their liquidity. In addition, over time, firms have adapted to use various tax policies introduced by the government in response to the pandemic to mitigate the adverse impacts of the crisis.

This study draws on a sample solely from one emerging country.

The results of this study can aid governments, policymakers, tax authorities and companies in evaluating their strategies concerning preparedness and emergency responses during crises, particularly those caused by pandemics.

To the best of the author’s knowledge, this study is considered one of the initial efforts to examine the impact of the COVID-19 pandemic on corporate tax avoidance in an emerging country like Indonesia.

Micro-regeneration can effectively enhance a neighborhood’s commercial vitality and serve as a viable approach to boost economic benefits. However, the small scale of micro-regeneration efforts and the fragmented nature of information currently limit the availability of strong empirical evidence demonstrating its impact on neighborhood commercial vitality. The aim of the study was to examine the link between micro-regeneration and neighborhood commercial vitality, focusing on the average, time-lag, spatial spillover, and spatial heterogeneity effects.

Using the panel data set of 1,755 neighborhoods in Chongqing from 2016 to 2021 as the research sample, the difference-in-differences (DID) method was employed in this study to explore the impact micro-regeneration has on neighborhood commercial vitality.

The results illustrate that: (1) micro-regeneration can promote neighborhood commercial vitality in terms of the number and types of local consumption amenities by 27.76 and 5.89%, respectively, with no time-lag effect; (2) the positive spillovers can exist within the range of 5,000 meters–5,500 meters of regenerated neighborhoods; and (3) the effect of micro-regeneration on neighborhood commercial vitality can be greater in peripheral areas than in core areas of the city.

The findings fill the knowledge gap on the relationship between micro-regeneration and neighborhood commercial vitality. Additionally, the results on the time-lag effect, spatial spillover effects, and spatial heterogeneity provide practical implications that can support the government and private sector in developing temporal and spatial arrangements for micro-regeneration projects.