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This study aims to use institutional and upper echelons theories to comprehensively investigate the intricate interplay between TMT legal expertise and firms' adaptive strategies in legal contexts, notably within emerging economies. It explores how upper echelons experiences shape opportunistic compliance strategies, impacting value and risk perceptions. Drawing on upper echelons theory, the research probes how TMT legal expertise molds firms’ involvement in significant lawsuits, accounting for influential roles. It scrutinizes TMT’s impact on legal strategies, positing that managerial discretion emerges from environmental factors, organizational attributes and executive traits. The study underscores TMT’s internal incentives and external factors’ interplay, molding strategic legal engagement.

To validate this framework, statistical analysis is performed on data from 2,584 Chinese-listed firms. The data set spans 2010–2015, with 5,713 material lawsuits. Chosen due to reliable institutional-level incentives data from the China Market Index Database, years 2016–2019 are excluded for methodological disparities. Moreover, 2007–2009 is omitted to mitigate the potential financial crisis impact. This study’s 11,272 observations ensure robust empirical exploration, offering insights into the interplay of TMT legal expertise, institutional factors and firms’ legal strategies.

The study reveals that firms led by executives with legal expertise are more prone to engage in significant lawsuits, indicating strategic use of legal skills. TMT age moderates this, with older teams less likely to engage. TMT tenure’s effect remains unclear due to tenure-risk preference complexity. Institutional factors matter; less legally mature regions reduce managers’ legal risk intention. Results confirm hypotheses and highlight executive human capital’s impact on firms’ legal strategies.

This study acknowledges contributions while highlighting limitations, including the need for detailed distinctions in lawsuit roles and exploration of heterogeneous TMT power dynamics. Further research is proposed for nuanced power dynamics and comprehensive TMT legal background data. The study advances upper echelons theory by introducing TMT legal expertise as a factor influencing strategic lawsuit behavior. It challenges institutional theory by showing the adaptable legal context, beyond fixed constraints. Moderating factors – group risk attitude and external knowledge – deepen understanding of upper echelons’ impact. Enhanced data collection is encouraged to address limitations and refine findings.

This study’s implications extend to managerial practices. Firms should acknowledge the dynamic legal system, using TMT legal expertise for strategic legal challenges. Executives should pragmatically approach regulations. While legal professionals enhance compliance, caution is needed in selecting TMT members with legal expertise due to the risk of misusing it for unnecessary litigation, potentially misaligned with financial performance goals.

This study combines institutional and upper echelons theories to explore TMT legal expertise’s impact on firms’ adaptive strategies in emerging economies. It challenges the idea of a universally constraining legal environment and highlights how TMT legal expertise enhances firms’ management of complex legal risks. The research introduces TMT legal expertise as an influencing factor in strategic lawsuits, revealing nuanced relationships between legal contexts and strategic decisions. The findings enrich upper echelons theory, challenge conventional institutional views and identify moderating factors that deepen the understanding of upper echelons’ influence in legal landscapes.

The conventional design method relies on a priori knowledge, which limits the rapid and efficient development of electronic packaging structures. The purpose of this study is to propose a hybrid method of data-driven inverse design, which couples adaptive surrogate model technology with optimization algorithm to to enable an efficient and accurate inverse design of electronic packaging structures.

The multisurrogate accumulative local error-based ensemble forward prediction model is proposed to predict the performance properties of the packaging structure. As the forward prediction model is adaptive, it can identify respond to sensitive regions of design space and sample more design points in those regions, getting the trade-off between accuracy and computation resources. In addition, the forward prediction model uses the average ensemble method to mitigate the accuracy degradation caused by poor individual surrogate performance. The Particle Swarm Optimization algorithm is then coupled with the forward prediction model for the inverse design of the electronic packaging structure.

Benchmark testing demonstrated the superior approximate performance of the proposed ensemble model. Two engineering cases have shown that using the proposed method for inverse design has significant computational savings while ensuring design accuracy. In addition, the proposed method is capable of outputting multiple structure parameters according to the expected performance and can design the packaging structure based on its extreme performance.

Because of its data-driven nature, the inverse design method proposed also has potential applications in other scientific fields related to optimization and inverse design.

Although phone calls are widely used by debt collection services to persuade delinquent customers to repay, few financial services studies have analyzed the unstructured voice and text data to investigate how debt collection call strategies drive customers to repay. Moreover, extant research opens the “black box” mainly through psychological theories without hard behavioral data of customers. The purpose of our study is to address this research gap.

The authors randomly sampled 3,204 debt collection calls from a large consumer finance company in East Asia. To rule out alternative explanations for the findings, such as consumers' previous experience of being persuaded by debt collectors or repeated calls, the authors selected calls made to delinquent customers who had not been delinquent before and were being called by the company for the first time. The authors transformed the unstructured voice and textual data into structured data through automatic speech recognition (ASR), voice mining, natural language processing (NLP) and machine learning analyses.

The findings revealed that (1) both moral appeal (carrot) and social warning (stick) strategies decrease repayment time because they arouse mainly happy emotion and fear emotion, respectively; (2) the legal warning (stick) strategy backfires because of decreasing the happy emotion and triggering the anger emotion, which impedes customers' compliance; and (3) in contrast to traditional wisdom, the combination of carrot and stick fails to decrease the repayment time.

The findings provide a valuable and systematic understanding of the effect of carrot strategies, stick strategies and the combinations of them on repayment time. This study is among the first to empirically analyze the effectiveness of carrot strategies, stick strategies and their joint strategies on repayment time through unstructured vocal and textual data analysis. What's more, the previous studies open the “black box” through psychological mechanism. The authors firstly elucidate a behavioral mechanism for why consumers behave differently under varying debt collection strategies by utilizing ASR, NLP and vocal emotion analyses.

The review aims to facilitate a broader understanding of platform opening and cooperation and points out potential research directions for scholars.

This study searches Web of Science (WOS) database for relevant literature published between 2010 and 2021 and selects 86 papers for this review. The selected literature is categorized according to three dimensions: the strategic choice of platform opening and cooperation (before opening), the construction of an open platform (during opening) and the impact of platform opening and cooperation (after opening). Through comparative analysis, the authors identify research gaps and propose four future research agendas.

The study finds that the current studies are fragmented, and a research system with a theoretical foundation has not yet formed. In addition, with the development of platform operations, new topics such as platform ecosystems and open platform governance have emerged. In short, there is an urgent need for scholars to conduct exploratory research. To this end, the study proposes four future research agendas: strengthen basic research on platform opening and cooperation, deeply explore the dynamic evolution and cutting-edge models of platform opening and cooperation, analyze potential crises and impacts of platform openness and strengthen research on open platform governance.

This is the first systematic review on platform opening and cooperation. Through categorizing literature into three dimensions, this article clearly shows the research status and provides future research avenues.

This study aims to identify factors that influence corporate environmental disclosure (CED) quality.

Using content analysis, an index and scoring scheme were applied to annual reports, stand-alone reports and corporate homepages of a sample of 116 oil and gas companies in 19 developing countries (DCs).

The results of this study reveal that out of 12 hypothesized variables, only 5 variables (company size, foreign ownership, profitability, leverage and membership of industry’s associations) are positively related to the CED quality.

The study has implications in enhancing the understanding of CED practices by oil and gas companies in DCs and the factors that influence the quality of such disclosure. Thus, the results of the study serve as input toward the development of improved regulations concerning CED for the oil and gas industry and provide guidelines to the regulators to make relevant decisions on social and environmental information items to be incorporated in the regulatory standards.

The current study attempts to fill the gaps in the literature by examining CED quality (rather than its quantity), concentrating on environmental disclosure made on the three main mediums of reporting. The study also extends previous research of CED by investigating some factors that have the potential to influence the content-quality of environmental disclosure, such as type of company (independent or constrain company) and industry’s association membership which have never been examined in the related literature.

Considering rapid digitalization development, this study examines the impacts of digital technology innovation on social responsibility in platform enterprises.

The study applies the event study method and cross-sectional regression analysis, taking 168 digital technology innovations for social responsibility issued by 88 listed platform enterprises from 2011 to 2022 to study the impact of digital technology innovations for social responsibility announcements of different announcement content and platform attributes on the stock market value of platform enterprises.

The results show that, first, the positive stock market reaction is produced on the same day as the digital technology innovation announcement. Second, the announcement of the platform’s public social responsibility and the announcement of co-innovation and radical innovation bring more positive stock market reactions. In addition, the announcements mentioned above issued by trading platforms bring more positive stock market reactions. Finally, the social responsibility attribution characteristics of the announcement did not have a significant differentiated impact on the stock market reaction.

Most scholars have studied digital technology innovation for social responsibility through modeling rather than second-hand data to empirically examine. This study uses second-hand data with the instrumental stakeholder theory to provide a new research perspective on platform social responsibility. In addition, in order to explore the different impacts of digital technology innovation on social responsibility, this study has classified digital technology innovation for social responsibility according to its social responsibility and digital technology innovation characteristics.

The purpose of this paper is to examine whether Fama–French common risk-factor portfolio investors herd on a daily basis for five developed markets, namely, Europe, Japan, Asia Pacific ex Japan, North America and Globe.

To examine the herd behavior of common risk-factor portfolio investors, this paper utilizes the cross-sectional absolute deviations (CSAD) methodology, covering a daily data sampling period of July 1990 to January 2019 from Kenneth R. French-Data Library. CSAD driven by fundamental and non-fundamental information is assessed using Fama–French five-factor model.

The results do not provide evidence for herding under normal market conditions, either when reacting to fundamental information or non-fundamental information, for any region under consideration. However, Fama–French common risk-factor portfolio investors mimic the underlying risk factors in returns related to size and book-to-market value, size and operating profitability, size and investment and size and momentum of the equity stocks in European and Japanese markets during crisis period. Also, no considerable evidence is found for herding (on fundamental information) under crisis and up-market conditions except for Japan. Ancillary findings are discussed under conclusion.

Further research on new risk factors explaining stock return variation may help improve the model performance. The performance can be improved by adding new risk factors that are free from behavioral bias but significant in explaining common stock return variation. Also, it is necessary to revisit the existing common risk factors in order to understand behavioral aspects that may affect cost of capital calculations (e.g. pricing errors) and valuation of investment portfolios.

This is the first paper that examines the herd behavior (fundamental and non-fundamental) of Fama–French common risk-factor investors using five-factor model.

This study aims to understand the epistemic foundation of the classification applied in the first Chinese library catalogue, the Seven Epitomes (Qilue).

Originating from a theoretical stance that situates knowledge organization in its social context, the study applies a multifaceted framework pertaining to five categories of textual data: the Seven Epitomes; biographical information about the classificationist Liu Xin; and the relevant intellectual, political, and technological history.

The study discovers seven principles contributing to the epistemic foundation of the catalogue's classification: the Han imperial library collection imposed as the literary warrant; government functions considered for structuring texts; classicist morality determining the main classificatory structure; knowledge perceived and organized as a unity; objects, rather than subjects, of concern affecting categories at the main class level; correlative thinking connecting all text categories to a supreme knowledge embodied by the Six Classics; and classicist moral values resulting in both vertical and horizontal hierarchies among categories as well as texts.

A major limitation of the study is its focus on the main classes, with limited attention to subclasses. Future research can extend the analysis to examine subclasses of the same scheme. Findings from these studies may lead to a comparison between the epistemic approach in the target classification and the analytic one common in today's bibliographic classification.

The study is the first to examine in depth the epistemic foundation of traditional Chinese bibliographic classification, anchoring the classification in its appropriate social and historical context.