Research Data Services Research Articles

Stewarding Contextual Integrity in Data Services for Indigenous Scholarship

ABSTRACTThe CARE Principles for Indigenous Data Governance provide essential guideposts for the stewardship of Indigenous data. To put CARE into practice in libraries and repositories, resources are needed to support implementation and integration into current research data services (RDS). Based on analysis of case studies with scholars of Indigenous language and culture, this paper articulates specific Indigenous research and data practices to guide metadata work and other areas of responsibility in RDS. The cases surface the richness of relationships and the significance of accountability in the research process—demonstrating the “relational accountability” inherent in Indigenous research methods. Robust representation of relationality is essential to retaining integrity of context in metadata for Indigenous research data. We consider the practical implications of documenting relational context with current descriptive metadata approaches and challenges in achieving CARE adherent metadata, which we argue is the backbone for broader application of CARE for Indigenous RDS.

Substitutive effects of accounting transparency and corporate governance on firm value

Recently, there has been increasing emphasis on the importance of accounting transparency and corporate governance. While previous studies have individually examined their impact on firm values, this paper investigates their combined effects. Using data from the Compustat and Center for Research in Security Prices databases obtained through Wharton Research Data Services, we measure accounting transparency and company value, and we employ the E-index as a measure of corporate governance. This study conducts firm, year, and industry fixed effects regressions on a large panel dataset comprising 21,476 firm-year observations. According to our research, both accounting transparency and good corporate governance have negative effects on the value of a company. This means that spending money to improve both may end up costing more than it benefits, which could lower the value of the company. These results support the notion that investors may see that maintaining high levels of accounting transparency and corporate governance can be excessively costly, while an overemphasis on these two metrics could potentially limit the flexibility and creativity of management. These findings have implications for managers tasked with achieving a balance between robust corporate governance, enhanced accounting transparency, and corporate value considerations.

Exploring the Relationship between Market Expansion and Corporate Innovation: The Mediating Role of Corporate Investment

This research delves into the dynamic interplay between market expansion and corporate innovation, underscoring the critical mediating role of corporate investments. In the backdrop of globalization and informatization, we explore how expanding into new markets drives firms to innovate, enhancing their competitive edge and market reach. Utilizing a robust dataset from Wharton Research Data Services encompassing 3838 North American companies from 2013 to 2023, we employ quantitative analysis and empirical methods to dissect these relationships. Our findings reveal that while market expansion directly necessitates and benefits from innovation, corporate investments in R&D significantly bolster this process by efficiently allocating resources to foster innovation and market penetration. Moreover, the research examines various factors such as corporate governance, financial health, and external market conditions that influence these dynamics, offering a comprehensive understanding of the strategies that companies can employ to sustain growth and innovation in a competitive landscape.

ESG performance, media coverage and brand value

PurposeThe performance of corporations in sustainable development is not only a concern of investors, but has also captured ever-increasing attention from consumers. However, the evidence on how these good practices would ultimately benefit brands economically remains insufficient. This study tests the causal effect between corporate Environmental, Social, and Governance (ESG) performance, media coverage, and brand value to reveal the underlying mechanisms of how consumers would react to high ESG performance.Design/methodology/approachThis study uses panel data regression analysis with a sample of Chinese A-share non-financial listed companies from 2010 to 2021. ESG performance, brand value, and media coverage are assessed with Huazheng ESG Rating, the rankings from the China’s 500 Most Valuable Brands' list published by the World Brand Lab, and media index compiled by the Chinese Research Data Services Platform (CNRDS) respectively.FindingsThis research confirmed that ESG performance positively impacted brand value in terms of profitability, and that media coverage played a role as a megaphone in this relationship. Large-scale corporates, compared to small ones, benefited more from good ESG ratings due to increased media coverage.Originality/valueThe findings provide evidence of the megaphone effect of media coverage on the relationship between firms’ ESG engagements and brand value in the product market, which has extended the knowledge of media’s monitoring role in the financial market. And this megaphone effect is strengthened by firm size in which larger firms have spotlight effect in draw public attention due to higher expectations in terms of social responsibility.

Do Cash-Flow Performance Metrics in CEOs’ Compensation Contracts Enhance Firm Innovation?

ABSTRACT Building on prior research showing a positive link between internally generated cash flows and innovation output, we hypothesize and find that the inclusion of cash-flow performance metrics in CEOs’ compensation contracts positively impacts firm innovation. Our findings are consistent with the theory that cash-flow metrics incentivize CEOs to make prudent investment decisions, thereby addressing both the overinvestment and the underinvestment challenges for firm innovation. As a result, firms utilizing cash-flow metrics exhibit lower innovation input and higher innovation output compared to firms not employing such metrics. Furthermore, our research indicates that the incentive effect of cash-flow metrics on firm innovation is more pronounced when cash flows convey unique information not reflected in earnings and when CEOs have longer expected employment horizons. This study underscores the effectiveness of cash-flow metrics in motivating management to promote firm innovation. Data Availability: Subscription-based data are obtained from Wharton Research Data Services; public data are obtained from public sources specified in the paper. JEL Classifications: D81, G30, J33, M52.

Returnees and innovation: evidence from Chinese publicly listed firms

Abstract As the Chinese economy shifts from factor-driven to innovation-driven growth, Chinese firms are increasingly lacking highly skilled talents. In this context, attracting high-skill returnees might represent an effective strategy to access knowledge. In this paper, we investigate the relationship between high-skill returnees and innovation of Chinese publicly listed firms. We construct a unique dataset of 2,499 firms over the period 2002–16 by combining three different data sources (i.e. Chinese Research Data Services Platform, China Stock Market & Accounting Research Database, and LinkedIn). Our results show that different typologies of returnees (employees, technologists, and managers) with different experiences abroad (work vs study) may bring back different skills and impact differently on firm innovation. Our main findings show that (1) returnee employees and technologists are positively associated with firm’s patenting; (2) returnees’ overseas work experience matters more than study experience; and (3) the positive role of returnees is subject to contingencies related to firm characteristics such as ownership, location, and size.

The impact of green finance on companies’ overcapacity: Evidence from green financial reform and innovation policy in China

The Green Financial Reform and Innovation (GFRI) policy not only serves as a crucial tool for facilitating a comprehensive transition towards sustainable practices in economic and social development but also has a significant influence on the operational activities of companies. However, there is limited research on whether the GFRI policy can affect companies’ overcapacity. To address this gap, we use data from the China Stock Market & Accounting Research Database and the Chinese Research Data Services Platform to evaluate the impact of green finance on companies’ overcapacity. The results show that the GFRI policy can effectively alleviate companies’ overcapacity. Moreover, the GFRI policy can alleviate companies’ overcapacity by strengthening market competition, enhancing financing constraints, and promoting innovation. Further, the GFRI policy exerts a more pronounced effect on alleviating overcapacity in non-labor-intensive companies, companies in heavily polluting industries, and companies in regions with weak environmental supervision. This paper not only supplements the relevant literature on alleviating overcapacity but also provides more theoretical support for promoting a national green financial system in practice.

Digital transformation and governance heterogeneity as determinants of CSR disclosure: insights from Chinese A-share companies

PurposeIn this fast-changing world, digitization has become crucial to organizations, allowing decision-makers to alter corporate processes. Companies with a higher corporate social responsibility (CSR) level not only help encourage employees to focus on their goals, but they also show that they take their social responsibility seriously, which is increasingly important in today’s digital economy. So, this study aims to examine the relationship between digital transformation and CSR disclosure of Chinese A-share companies. Furthermore, this research investigates the moderating impact of governance heterogeneity, including CEO power and corporate internal control (INT) mechanisms.Design/methodology/approachThis study used fixed effect estimation with robust standard errors to examine the relationship between digital transformation and CSR disclosure and the moderating effect of governance heterogeneity among Chinese A-share companies from 2010 to 2020. The whole sample consists of 17,266 firms, including 5,038 state-owned enterprise (SOE) company records and 12,228 non-SOE records. The whole sample data is collected from the China Stock Market and Accounting Research, the Chinese Research Data Services and the WIND databases.FindingsThe regression results lead us to three conclusions after classifying the sample into non-SOE and SOE groups. First, Chinese A-share businesses with greater levels of digitalization have lower CSR disclosures. Both SOE and non-SOE are consistent with these findings. Second, increasing CEO authority creates a more centralized company decision-making structure (Breuer et al., 2022; Freire, 2019), which improves the negative association between digitalization and CSR disclosure. These conclusions, however, also apply to non-SOE. Finally, INT reinforces the association between corporate digitization and CSR disclosure, which is especially obvious in SOEs. These findings are robust to alternative HEXUN CSR disclosure index. Heterogeneity analysis shows that the negative relationship between corporate digitalization and CSR disclosures is more pronounced in bigger, highly levered and highly financialized firms.Originality/valueDigitalization and CSR disclosure are well studied, but few have examined their interactions from a governance heterogeneity perspective in China. Practitioners and policymakers may use these insights to help business owners implement suitable digital policies for firm development from diverse business perspectives.

Research data management in selected East African libraries: A survey

This article investigates the knowledge of research data management and services among library professionals in selected East African libraries. A survey research design was employed, and data was collected using a structured questionnaire from 180 respondents representing four East African countries: Malawi, Mozambique, Zambia and Zimbabwe. The findings reveal that only 31.1% of the selected East African librarians agreed that their institutional libraries provided research data management services. The standard research data management services offered by their libraries included data publishing, sharing and reuse, while collaboration with academic programmes was identified as an essential approach for research data management skill development. The study highlights the need for librarians to acquire legal, policy and advisory skills and knowledge of institutional and extra-institutional resources and the research life cycle for effective research data management service delivery.

Investigating supply chain participants’ circular economy action effects on firm financial performance from a stakeholder theory perspective

The circular economy (CE) model has recently emerged and been practiced by many firms due to pressure for desirability of sustainability. It is important for researchers and practitioners to understand the effects of firms' CE actions on their own and their supply chain partners' financial performance to better implement CE practices. However, existing literature has not analyzed the impact of firms' CE actions from a holistic supply chain framework to find how a firm's CE actions affect its supply chain partners' financial performance. In this study, we investigate this issue from the stakeholder theory perspective with empirical evidence from the China Stock Market and Accounting Research database and the Chinese Research Data Services Platform database for 2006–2021 to perform regressions. Our findings suggest a firm's CE actions have both a positive direct effect to improve its own financial performance and a spillover effect to enhance its partners' (i.e., suppliers' and customers') financial performance. Additionally, a higher supply chain concentration level strengthens the CE direct effect. Further, we find a positive moderating influence of industry competition on stimulating the CE direct effect and its positive moderating effect on the spillover effect of downstream customers' CE efforts on the financial performance of the focal firms. Our findings urge firms to implement and encourage their partners to make CE efforts to enhance their financial performance. Firms should also strengthen centralized purchasing and supply with downstream partners to utilize the amplified CE efforts' effect, particularly when they face fierce competition.

Comparative Analysis of NLP-Based Models for Company Classification

The task of company classification is traditionally performed using established standards, such as the Global Industry Classification Standard (GICS). However, these approaches heavily rely on laborious manual efforts by domain experts, resulting in slow, costly, and vendor-specific assignments. Therefore, we investigate recent natural language processing (NLP) advancements to automate the company classification process. In particular, we employ and evaluate various NLP-based models, including zero-shot learning, One-vs-Rest classification, multi-class classifiers, and ChatGPT-aided classification. We conduct a comprehensive comparison among these models to assess their effectiveness in the company classification task. The evaluation uses the Wharton Research Data Services (WRDS) dataset, consisting of textual descriptions of publicly traded companies. Our findings reveal that the RoBERTa and One-vs-Rest classifiers surpass the other methods, achieving F1 scores of 0.81 and 0.80 on the WRDS dataset, respectively. These results demonstrate that deep learning algorithms offer the potential to automate, standardize, and continuously update classification systems in an efficient and cost-effective way. In addition, we introduce several improvements to the multi-class classification techniques: (1) in the zero-shot methodology, we TF-IDF to enhance sector representation, yielding improved accuracy in comparison to standard zero-shot classifiers; (2) next, we use ChatGPT for dataset generation, revealing potential in scenarios where datasets of company descriptions are lacking; and (3) we also employ K-Fold to reduce noise in the WRDS dataset, followed by conducting experiments to assess the impact of noise reduction on the company classification results.

Analyst coverage and manufacturing enterprise green transition: An empirical study based on Chinese enterprises.

This study used the entropy weight method to develop an index of green transition and empirically examined the influence of analyst coverage on green transitions by manufacturing enterprises in China. We examined A-share listed manufacturing firms from 2010-2020, using patent data, media reports from Chinese Research Data Services, and other data from the Cathay Capital Database. After excluding cases with missing data, our final sample comprised 16,576 observations. The following conclusions were drawn. First, analyst coverage significantly contributed to green transition. Second, the analysis of the impact mechanism showed that improving information transparency, weakening principal-agent conflict, and increasing environmental legitimacy pressure are the paths through which analyst coverage affects manufacturing's corporate green transition. Third, the effect of analyst coverage was stronger for large-scale and state-owned manufacturing companies.

Do anti-takeover provisions restrain IPO underpricing? An analysis from the perspective of information asymmetry

PurposeBased on the textual-analyzed data covering 2148 IPO firms in China’s stock market during the 2007–2018 period, the authors’ purpose is to examine the influence of anti-takeover provision (ATP) adoption on initial public offerings (IPO) underpricing and identify the reducing effect of the former.Design/methodology/approachThe authors examine the sample consisting of Chinese A-share listed IPO firms between 2007 and 2018 from China Stock Market Accounting Research and Chinese Research Data Services, with ATP data collected from the IPO firm chapters. Specifically, the authors use text analysis to identify whether there are ATPs in the IPO firm chapters, as well as the number of ATPs. H1: IPO underpricing is less severe for firms adopting ATPs. H2: The effect of ATP adoption on IPO underpricing is more salient for firms in worse information environments.FindingsThe authors examine the influence of ATP adoption on IPO underpricing and identify the reducing effect of the former. This effect can be explained by the fact that adopting ATPs in IPO firm chapters can reduce information asymmetry to a large extent by helping external investors obtain more private information, which alleviates IPO underpricing. The authors also find that the reducing effect is more significant in the worsened information environment. Furthermore, the authors explore the influence of adopting ATPs on other IPO characteristics and find positive effects on IPO over-subscription, funds raised and trading activity and negative effects on listing fees.Originality/valueThis study mainly contributes to the literature from the following two aspects. First, the study enriches the literature about the influencing factors of IPO underpricing. Second, the study also enriches the literature about the economic consequences of ATP adoption. This study also has important policy implications. With the coming of the era of decentralized ownership in China’s capital market, ATP adoption has become more important and attracted more attention. Also, investors focus more on pricing efficiency. The findings in this paper provide a more comprehensive understanding of the relationship between ATP adoption and IPO underpricing.

The program efficiency of environmental and social non-governmental organizations: A comparative study.

Non-governmental organizations (NGOs) are becoming increasingly significant stakeholders in global governance and business operations. However, measuring their efficiency is a challenging task due to their mission-driven nature. While previous research has proposed financial and non-financial indicators to measure NGO efficiency, none of them has compared the differences between environmental and social NGOs. This study aims to investigate the factors influencing the program efficiency of NGOs in China and compare the differences between environmental and social NGOs. 12 indicators are employed and tested using data collected from the Chinese Research Data Services (CNRDS) platform. The study employs multiple regression analysis to examine the influential factors identified in the dataset. The findings demonstrated different influential factors of program efficiency among environmental and social NGOs. The results of the analysis provide valuable insights for NGO operators, policymakers, and researchers in the field of NGOs.

Ecological Interpretation of Regional Elements in the Design of Local and Korean Urban Public Facilities

In the planning and construction of modern city, how to highlight the regional elements of the city and show the ecological design of the city has become the key content. Public facility design is an important part of urban construction. On the one hand, it is directly related to the public life, and on the other hand, it is closely related to the urban context and future development. Based on this, this paper mainly discusses the regional elements and ecological interpretation in the design of urban public facilities in our country and South Korea, aiming to provide reference for the subsequent design. To answer our research question, we begin by constructing a sample of all Chinese non- financial firms listed in the A-share market between 2007 and 2018 (n=29,181). We obtain financial statement data from the China Stock Market and Accounting Research (CSMAR) database. We obtain individual audit partner’s demographic information, such as gender and educational background, from the Chinese Research Data Services CARD database and the China Institute of Certified Public Accountants (CICPA) website. We lose 1,674 observations because we cannot find the signing auditor’s demographic information needed in the Heckman’s model and PSM; we further delete 4,783 observations because they do not have enough firm financial data to compute the variables used in our empirical tests; we exclude 672 observations because the annual reports are signed with three auditor signatures. Our final sample consists of 22,052 firm-year observations.

Using Data Interviews to Map Research Data Services (RDS) Needs at a Health Science Institution

Objective: On January 21, 2023 the National Institutes of Health Data Management and Sharing Policy (NIH DMSP 2023) went into effect. Ahead of these expanded data sharing requirements, the Health Sciences Library at the University of Tennessee Health Science Center (UTHSC) set out to better understand health science research data service (RDS) needs and how the Library could develop new services in alignment with existing institutional support.Methods: Building upon a set of data interview questions created by Read et al. (2015), the author conducted a series of semi-structured interviews with 10 researchers in the fall of 2022. The interview transcripts were inductively coded and analyzed for themes relevant to RDS needs.Results: Analysis of the interviews identified several opportunities for the Library to take ownership of RDS activities in the areas of support services, training and instruction, and tools and technology. Additionally, these interviews helped to characterize existing institutional support and to develop an informal community of practice at the University of Tennessee Health Science Center (UTHSC). These RDS topics were mapped onto Harvard’s Biomedical Data Lifecyle to further clarify areas of need and opportunities for growth for the Library and institutional RDS partners. Conclusions: The study identified critical RDS needs among UTHSC researchers and highlighted the potential role of the Library as a valuable partner in addressing these needs and fostering collaboration among campus stakeholders. Conversations informed by these findings continue with stakeholders about the best way to meet the needs of researchers on campus as well as centralize and coordinate existing endeavors.

Investigating the Influence of Green Technology Innovations on Energy Consumption and Corporate Value: Empirical Evidence from Chemical Industries of China

This study investigates the impact of green technology innovation on energy consumption and corporate value in China's chemical industries, utilizing data from 133 A-share listed enterprises over the period of 2013-2022. Data on green technology innovation are sourced from the Chinese Research Data Services (CNRDS) Platform, while energy consumption data are obtained from listed companies' annual reports, social responsibility reports, and websites. Corporate ESG data is sourced from Huazheng ESG rating, and other data are obtained from China Stock Market & Accounting Research Database (CSMAR) and the National Statistics Bureau. Employing a two-way fixed effects model, this finds that green technology innovation mitigates energy consumption, and boosts corporate value of Chinese A-share listed enterprises of chemical industries. The study ensures the robustness of its findings through 2SLS analysis, using green patent applications as instrumental variables. The moderating effects of ESG disclosure are found significant to strengthen the influence of green technology innovation on energy consumption and corporate value. These findings offer valuable insights for policymakers and stakeholders in shaping strategies to promote sustainability and competitiveness in the chemical industry.

Fintech Key-Phrase: A New Chinese Financial High-Tech Dataset Accelerating Expression-Level Information Retrieval

Expression-level information extraction is a challenging task in natural language processing (NLP), which aims to retrieve crucial semantic information from linguistic documents. However, there is a lack of up-to-date data resources for accelerating expression-level information extraction, particularly in the Chinese financial high technology field. To address this gap, we introduce Fintech Key-Phrase: a human-annotated key-phrase dataset for the Chinese financial high technology domain. This dataset comprises over 12K paragraphs along with annotated domain-specific key-phrases. We extract the publicly released reports, Chinese management’s discussion and analysis (CMD&A), from the renowned Chinese research data services platform (CNRDS) and then filter the reports related to high technology. The high technology key-phrases are annotated following pre-defined philosophy guidelines to ensure annotation quality. In order to better understand the limitations and challenges in the purposed dataset, we conducted comprehensive noise evaluation experiments for the Fintech Key-Phrase, including annotation consistency assessment and absolute annotation quality evaluation. To demonstrate the usefulness of our released Fintech Key-Phrase in retrieving valuable information in the Chinese financial high technology field, we evaluate its significance using several superior information retrieval systems as representative baselines and report corresponding performance statistics. Additionally, we further applied ChatGPT to the text augmentation approach of the Fintech Key-Phrase dataset. Extensive comparative experiments demonstrate that the augmented Fintech Key-Phrase dataset significantly improved the coverage and accuracy of extracting key phrases in the finance and high-tech domains. We believe that this dataset can facilitate scientific research and exploration in the Chinese financial high technology field. We have made the Fintech Key-Phrase dataset and the experimental code of the adopted baselines accessible on Github: https://github.com/albert-jin/Fintech-Key-Phrase. To encourage newcomers to participate in the financial high-tech domain information retrieval research, we have developed a series of tools, including an open website1and corresponding real-time information retrieval APIs.2

Information technology maturity and acceptance models integration: the case of RDS

Purpose/Thesis: This article presents three methods for evaluating technological innovations, with a particular focus on information technology innovations such as information systems and services. These methods include assessing maturity, the diffusion of innovative information technologies, and their acceptance. The aim is to indicate the complementarity of these methods, which makes their integrated application appropriate.
 Approach/Methods: There are two types of theories that take into account the perspectives of both the creators of information technologies (technology maturity models) and their users (technology acceptance models). The article attempts to identify the commonalities between these models to enable their joint use. The joint application of these models is demonstrated through an example using research data services (RDS) maturity models. Six RDS maturity models known from the literature were analyzed from the perspective of their completeness using the technology acceptance model (TAM).
 Results and conclusions: This approach enables the assessment of maturity models from the perspective of RDS users’ needs, affecting the level of their acceptance. The results indicate that existing RDS maturity models do not sufficiently consider the needs and perspectives of their users, hindering the acceptance of the technology.
 Originality/Value: The article demonstrates a new point of view on information systems assessment, which serves to integrate two previously used methods of assessing the IS: maturity models and innovative information technologies acceptance models. The article proposes combining both methods to obtain a more coherent and universal research tool.

Creating a Scholarly API Cookbook: Supporting Library Users with Programmatic Access to Information

Scholarly web-based application programming interfaces (APIs) allow users to interact with information and data programmatically. Interacting with information programmatically allows users to create advanced information query workflows and quickly access machine-readable data for downstream computations. With the growing availability of scholarly APIs from open and commercial library databases, supporting access to information via an API has become a key support area for research data services in libraries. This article describes our efforts with supporting API access through the development of an online Scholarly API Cookbook. The Cookbook contains code recipes (i.e., tutorials) for getting started with 10 different scholarly APIs, including for example, Scopus, World Bank, and PubMed. API tutorials are available in Python, Bash, Matlab, and Mathematica. A tutorial for interacting with library catalog data programmatically via Z39.50 is also included, as traditional library catalog metadata is rarely available via an API. In addition to describing the Scholarly API Cookbook content, we discuss our experiences building a student research data services programming team, challenges we encountered, and ideas to improve the Cookbook. The University of Alabama Libraries Scholarly API Cookbook is freely available and hosted on GitHub. All code within the API Cookbook is licensed with the permissive MIT license, and as a result, users are free to reuse and adapt the code in their teaching and research.