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This study aims to analyze the relationship between economic complexity (EC) and the green economy (GE) with the real and accrual earnings management (REM and AEM) of the listed companies on the Iranian stock exchange. The authors study whether EC and the GE can affect REM and AEM.

The authors used a multiple regression model based on the panel data and a fixed effect model to test hypotheses. The sample includes 1,351 companies listed on the Tehran Stock Exchange from 2014 to 2021.

The results show a positive and significant relationship between EC and the GE with REM and AEM.

Considering the importance of a GE and since this research is the first to address the mentioned topic in emerging markets, it provides helpful insights for financial statement users, analysts and legal entities. Our study fills the literature gap and promotes knowledge regarding its relevant literature. Examining this relationship portrays the latest research perspectives in this field. The information from this study can assist in environmental management decision-making and relevant policymaking, promoting the movement toward sustainable development.

The purpose of this paper is to present an integrated data-driven framework for processing and analyzing large-scale vehicle maintenance records to get more comprehensive understanding on vehicle quality.

We propose a framework for vehicle quality analysis based on maintenance record mining and Bayesian Network. It includes the development of a comprehensive dictionary for efficient classification of maintenance items, and the establishment of a Bayesian Network model for vehicle quality evaluation. The vehicle design parameters, price and performance of functional systems are modeled as node variables in the Bayesian Network. Bayesian Network reasoning is then used to analyze the influence of these nodes on vehicle quality and their respective importance.

A case study using the maintenance records of 74 sport utility vehicle (SUV) models is presented to demonstrate the validity of the proposed framework. Our results reveal that factors such as vehicle size, chassis issues and engine displacement, can affect the chance of vehicle failures and accidents. The influence of factors such as price and performance of engine and chassis show explicit regional differences.

Previous research usually focuses on limited maintenance records from a single vehicle producer, while our proposed framework enables efficient and systematic processing of larger-scale maintenance records for vehicle quality analysis, which can support auto companies, consumers and regulators to make better decisions in purchase choice-making, vehicle design and market regulation.

The study aims to embrace the lingering call for more empirical studies that can theorize the role of digital platforms in digital entrepreneurship. Hence, this study seeks to reveal the liminal space entrepreneurial experience of third-party application developers, by investigating how the platform boundary resources promote third-party entrepreneurial actions, as they transition through the disoriented, uncertain and ambiguous processes of digital entrepreneurship development.

To conduct this investigation, an expert interview qualitative method was used. This approach is a well-established technique in the field of social sciences, which allowed a detailed exploration of the theory of liminality. Liminality refers to the transitional phase that individuals or groups experience when moving from one social or cultural context to another. The expert interview method is appropriate for this study because it involves engaging with knowledgeable individuals who have extensive experience and expertise in the subject area being investigated. Through in-depth and unstructured interviews, the experts were able to provide valuable insights and perspectives about the phenomenon investigated.

The research findings demonstrate that digital platform boundary resources play a significant role in the behaviour of third-party developers’ who engage in the development of digital entrepreneurship in today’s market. The study highlights three ways that show how these resources (software development kit (SDK), API, integrated development environment (IDE), libraries, frameworks) enable third-party developers to create new applications that are used to pursue entrepreneurship in a digital platform, leading to increased user engagement and revenue generation.

The research addresses the critical roles of digital platform boundary resources in digital entrepreneurship development processes. Also, using liminality theory, the research explicated the core experiences of third-party developers as they navigated the challenges and ambiguities experienced in the pursuit of entrepreneurship. Thus, contributing to the existing body of knowledge in literature and practice.

This exploratory, descriptive study examines the factors that might influence the success or failure of online peer production communities during their inception stage. It focuses on community-building activities, the roles of users and the dynamics of user interaction, aiming to shed light on practices that could contribute to a community's success at the inception stage.

By comparing two Stack Exchange Q&A communities with the same timelines but opposite outcomes during their beta testing phases, the research utilizes quantitative methods to categorize community activities, define user roles via k-means cluster analysis and examine interaction networks using social network analysis.

Our findings suggest the successful Mathematics Q&A community exhibited several distinct practices during its inception, such as the utilization of both external and internal tools, the development of community-specific tutorials and the strategic use of flagging functions for moderation. Eight user roles were discerned, with roles like content editors, metadata curators and gatekeepers being particularly prominent in the successful community. Additionally, a more densely interconnected user network characterized by active participation was observed in the successful community.

Concentrating on the inception stage of online communities, this study uncovers insights into the dynamics at play in the early life of peer production environments and provides empirical observations that may assist in shaping strategies for new online communities. It stands out by comparing communities within the same period to understand factors that may influence their early success.

Given the overlapping themes and periods in specific subjects within Islamic economics and finance bibliometric research, which may yield similar findings in bibliometric studies, it is essential to document the growth of Islamic economic and financial research using bibliometric methodologies. This study aims to understand better the critical bibliometric review trends and scientific advancements in Islamic economics and finance.

This study uses bibliometric analysis, collecting 46 Islamic economics bibliometric papers from the Web of Science Core Collection from 1975 to 2022. The authors generated top scientific scholars, keyword analysis, citation analysis, content analysis and conclusions for journal development using R Biblioshiny, VOSviewer, ATLAS.ti and Excel.

This study has established a comprehensive bibliometric framework for Islamic economics and finance bibliometric papers, encompassing all critical areas within the discipline and identifying any remaining research gaps. The major significant areas revealed were Islamic social finance and microfinance concerns, which are closely pertinent to the issues of ethics, corporate social responsibility and sustainability, respectively. The authors also identified opportunities for future bibliometric analyses in Islamic economics and finance, which include using more comprehensive databases, refining or broadening search strategies, using advanced techniques and units of analysis and suggesting themes for further exploration.

The study relies merely on the Web of Science Core Collection database, which provides the most in-depth citations by source for the world’s scientific and scholarly research. Future research may consider expanding its scope to include other databases for a broader range of sources. Furthermore, due to the rise of bibliometric studies in Islamic economics and finance, this study also comments on the saturation of bibliometric studies conducted in several similar areas. While researchers bring their unique analytical perspectives to bibliometrics, this study provides a comprehensive view of existing research in Islamic economics and finance, highlighting well-explored topics and those that remain less studied. Thus, this could assist researchers in determining their future research priorities.

Policymakers in Islamic financial and economic institutions, including banking institutions, social, financial institutions and halal institutions, should be impacted by this research when making policies or conducting research. The viability of the current Islamic economic and financial ecosystem will be indirectly maintained and managed by these implications.

This comprehensive meta-analysis in Islamic economics and finance is expected to impact the development and sustainability of the Islamic economic and financial ecosystem, promoting societal welfare through applying Islamic economics and finance.

This pioneering bibliometric analysis of Islamic economics and finance papers aims to offer insights and projections for future research in the field. This research contributes to the literature by examining various aspects, including evaluating literature on trending topics, analyzing papers related to research areas and conducting content analysis of existing bibliometric studies in Islamic economics and finance. It specifically groups these studies around fundamental topics, summarizes findings from contemporary research and identifies emerging research gaps.

The research attempts to estimate how the use of WeChat, the most popular mobile social networking application in contemporary China, affects rural household income.

Our materials are 4,552 rural samples from the Chinese General Social Survey, and a treatment effect (TE) model is employed to address the endogeneity of WeChat usage.

The results prove that WeChat usage has a statistically significant and positive correlation with rural household income. This conclusion remains robust after using alternative variables to replace the explanatory and dependent variables. Our research provides two channels through which WeChat usage boosts rural household income, namely, it can promote their off-farm employment and participation in investment activities.

Theoretically, the study provides several micro-evidences for understanding the impact of mobile social networks on rural household welfare. Further, our findings may shed light on the importance of digital technology applications in rural poverty alleviation for developing countries.

The integration of e-commerce platforms and artificial intelligence (AI) into the marketing channel ecosystem is challenging the explanatory capacity of traditional channel power theories, indicating a significant yet unaddressed research gap concerning the impact of these digital entities and AI on channel power exercise dynamics. This study adopts a contingency perspective to critically revisit how e-commerce platforms exercise channel power and the ensuing effects on channel conflicts. The purpose of this study is to extend the boundaries of traditional channel power theories, enhancing their relevance in today’s digital marketplace.

Building on channel power theories, the authors developed a framework tested with survey data collected from 262 sellers. This framework incorporates three key contingent variables: inter-platform competition, AI capabilities and platform value co-creation. Regression analysis was used to perform the analyses.

This study finds that intense inter-platform competition mitigates the (positive) negative relationship between platform channel power and the exercise of (non-) coercive power. Moreover, a platform’s AI capabilities and value co-creation activities diminish the potential for channel conflicts induced by the exercise of coercive power. AI capabilities can also strengthen the negative relationship between the exercise of non-coercive power and channel conflicts.

This study contributes to the advancement of traditional channel power theories by integrating contemporary digital elements like AI and platform dynamics. This study provides theoretical and practical insights on navigating channel power in modern marketing environments, offering strategic guidelines for optimizing channel relationships.

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.

This paper aims to explore the effects of director network on open innovation. As an informal institutional arrangement, the director network is an important source for the enterprise to obtain external information, which provide resource basis for open innovation. Chief Executive Officer (CEO) as the top of management team could make short-sighted decisions for personal interests; this paper also investigates the moderating role of CEO short-sightedness between director network and open innovation.

This paper takes 4,102 Chinese listed companies from 2007 to 2020 as the research sample. By introducing network centrality and structural hole to measure director network and using data mining to extract key words related to CEO short-sightedness from annual reports, this paper constructs several multiple linear regression models to analyze the impact of director network on open innovation and the moderating role of CEO short-sightedness.

The analysis finds that director network can facilitate corporate open innovation. Enterprises can acquire more external resources in high centrality and structural hole of director network and promote ability for corporate open innovation. The relationship between director network and open innovation is negatively moderated by CEO short-sightedness. When the level of corporate governance and analyst attention is high, the negative effect of CEO short-sightedness on the innovation effect of directors’ networks is suppressed.

This is the first empirical paper to investigate the promotion effect of director network on open innovation as well as the negative moderating role of CEO short-sightedness. The findings bring new perspectives to the open innovation and enlightenments for practical activities from social relationship aspect.

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.