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This study aims to investigate the quality of disclosure of a cutting-edge reporting tool – integrated reporting (<IR>) – in terms of its effectiveness to report on COVID-19 pandemic information, its ability to provide forward-looking information and risk impact implications, and its quality determinants in challenging times.

Thanks to a content analysis of 247 <IR> for FY20, an integrated reporting disclosure score was developed to assess the disclosure quality provided by the sampled companies. Three research questions were tested through logistic regressions.

Non-financial disclosure activities struggle to provide adequate information in terms of potential future scenarios, risk assessment and forward-looking analyses. However, companies incorporated in “Anglo-Saxon” territories drafted integrated reports of higher quality. More recently, incorporated companies have made a greater effort to measure and report COVID-19 pandemic impacts on environmental, social and governance and business activities, also increasing their risk assessment and mitigation efforts. Concerning the determinants of disclosure quality, leverage, corporate governance structures, country of incorporation and belonging to “high impact” industries all lead to a higher quality of <IR> disclosure.

Examining in detail corporate social responsibility activities and corporate governance integrity is pivotal to orienting strategy towards sustainable trajectories: to do so, corporate reporting and disclosure practices are essential tools. In this context, corporate governance systems that emphasize board diversity are proven, even in disruptive circumstances, to play a crucial role in providing corporate reports of higher quality. High disclosure quality that goes beyond mere financial results is considered to be necessary to remain competitive strategically, socially and environmentally.

Big data marketing analytics (BDMA) has been discovered to be a key contributing factor to developing necessary marketing capabilities. This research aims to investigate the impact of the technology and information quality of BDMA on the critical marketing capabilities by differentiating between firms with low and high perceived market performance.

The responses were collected from marketing professionals familiar with BDMA in North America (N = 236). The analysis was done with partial least squares-structural equation modelling (PLS-SEM).

The results indicated positive and significant relationships between the information and technology quality as exogenous constructs and the endogenous constructs of the marketing capabilities of marketing planning, implementation and customer relationship management (CRM) with mainly moderate effect sizes. Differences in the path coefficients in the structural model were detected between firms with low and high perceived market performance.

This research indicates the critical role of technology and information quality in developing marketing capabilities. The study discovered heterogeneity in the sample population when using the low and high perceived market performance as the source of potential heterogeneity, the presence of which would likely cause a threat to the validity of the results in case heterogeneity is not considered. Thus, this research builds on previous research by considering this issue.

This study aims to examine the effect of sustainability report quality (SRQ) on information risk. This research also aims to examine the effect of SRQ on stock market participation through information risk.

The research sample includes 120 firm-years listed on the Sri Kehati Index period of 2017–2021. The hypothesis test uses firm and industry effect regression analysis. SRQ is measured by the existence of a sustainability committee and external assurance. The information risk is measured by bid-ask spread. Stock market participation is measured by volume of stock trading.

Based on the data analysis, this investigation finds that SRQ reduces information risk. This research also finds that SRQ improves stock market participation by reducing information risk.

First, this examination gives new evidence of SRQ to promote information environment improvement. Second, this examination contributes to providing the role of SRQ in an emerging market, such as Indonesia. Third, this examination contributes to providing the evaluation standard for sustainability reporting quality in Indonesia, since Indonesia has no specific standard for the sustainability report. Fourth, this examination contributes to filling the previous gap.

The purpose of this study is to examine the quality of information in articles in the online encyclopedia, Wikipedia, as perceived by readers and content contributors. This study explored several dimensions and characteristics of information presented in Wikipedia by identifying new emerging dimensions in terms of readers’ perceptions of the quality of online information.

Two rounds of online surveys were conducted using a mixed-method approach. In the first survey, the authors conducted content analysis on 197 participants, and in the second survey, the authors conducted factor analysis on 107 study participants. The authors used Qualtrics Panel Services to recruit individuals who read and/or edited the English version of Wikipedia articles and resided in the United States.

The mixed-method approach employed in this study to explore the quality of online information had three core components: users’ perceptions of information quality, content analysis, and exploratory factor analysis of the perceived information quality structure. The study found a new information quality category, social aspect quality. Dimensions include fun, goodness, empowering and user generated.

The results demonstrate the emergence of novel quality attributes for information quality presented online, particularly in social media. Moreover, this study is one of the rare studies to employ a mixed-method approach that offers diverse but reliable perspectives on information quality as perceived by everyday users of Wikipedia.

This paper aims to conduct a systematic review of the literature on the quality of integrated reports (IR) and highlight the gaps in the existing research to provide directions and suggestions for future research.

This study was conducted through a systematic literature review using content analysis based on 40 papers from the Scopus, Web of Science and EBSCOhost databases on IR quality. While reading the full-text papers, the authors found six additional papers referenced by the literature being reviewed that were relevant to IR quality. Thus, there were 46 papers in the final review. The analysis begins with the definition and dimension of IR quality and theoretical lenses. Furthermore, this study outlines constructs or variables used in the previous literature.

The authors found that most studies used the quantitative method (41 papers or 89%). Five papers in the literature used qualitative methods (11%). Most researchers (34 papers or 72%) defined IR quality as consistent with the International Integrated Reporting Council framework, specifically the eight content elements. In particular, with the constructs that make up the quality of the IR, variations between researchers were found. Furthermore, there were some gaps that could be the directions for future research.

The literature that provides academic knowledge about IR quality is still limited, and research on IR is still growing. The literature review conducted by this study can provide an overview of the current research positions on the quality of IR and directions for future research in this area.

This study intends to show corporate executives a framework demonstrating the quality of corporate reporting. It can impact not only investors as a specific stakeholder group but also other stakeholder groups.

To the best of the authors’ knowledge, this study is the first literature review to examine the quality of IR, thus providing a map of current research to suggest directions for future research. Most of the previous literature reviews have been focused on integrated reporting (IR) in general and not quality.

Companies are increasingly implementing business process management systems (BPMSs) to support their processes. However, there is a gap in the literature regarding whether users also use BPMSs to manage the knowledge needed for processes to be completed. This study aims to analyze the factors that cause users to use BPMSs to manage the knowledge required in business processes.

The paper proposes an original model that integrates two successful information system models applied to BPMSs and knowledge management systems. To test the hypotheses derived from this new model, data were collected from 242 mature BPMS users from 12 Spanish and Latin American companies. Structural equation modeling with AMOS was used to examine the model.

Users’ perceived usefulness of a BPMS when using it for knowledge management (KM) is the only factor influencing them to use it for KM.

This study has practical implications for managers wishing to successfully implement a BPMS to support processes and for employees to use the knowledge embedded in the tool. The latter will only happen if users perceive the tool’s usefulness for KM.

To the best of the authors’ knowledge, this model is the first empirically validated model to successfully analyze BPMS users’ tendency to use BPMSs as a tool to support necessary KM in processes.

To study these issues, the authors chose a GFSC with one producer and one material supplier as research object, the supplier will offer green material to the producer and the producer will make green food using green production technology. Then, the authors proposed that consumers' perceived value was determined by the trustworthiness levels of the related green and quality-safety information provided by the supplier and the producer. Then, considering the trustworthiness levels of the green and quality information provided by the supplier and the producer, the authors improved the demand function. Afterwards, we constructed four investment models and their income models are built and then a cost-sharing and revenue-sharing contract (hereafter, CSRS) was adopted to coordinate the GFSC.

With the growth of consumers environmental awareness and life level, consumers' requirements for green and high quality food are growing. In recently years, to increase consumers' perceived trustworthiness on the product greenness and quality levels, stakeholders in green food supply chain (hereafter, GFSC) start to adopt the blockchain-based traceability system (hereafter, BLTS). For investors, they need to know the investment conditions and how to coordinate the GFSC.

(1) When the revenue-sharing coefficient is less than three-fourths and higher then a certain vaule, the cost-sharing and revenue-sharing contract can make the GFSC coordinate. (2) The investment cost threshold of the BLTS has a positive relationship with the trustworthiness improvement levels of the green and quality information, the green degree of food products and the quality of food products. (3) In the proposed four investment situations, as the growth of consumers perceived credibility coefficient about the greenness information and the quality information, chain members' revenues will increase. In addition, comparing with co-investing the BLTS, benefits of chain members are lower than them in the sole investment model.

(1) The demand function we proposed can help chain members forecast market demand to support production or ordering decisions. (2) The investment decision policies can offer a theoretical reference for chain members to use the BLTS. (3) The CSRS will offer the theoretical reference for coordinating the supply chain after using the BLTS. Furthermore, our study method can be referenced by other scholars. (4) The study method can offer a method reference for researchers who do a similar discussion in a manufacturing supply chain. Although, our research cannot guide the industrial practices, it can serve as a reference of the similar research in industry.

There is a growing importance of business intelligence systems (BIS) adoption in today’s digital economy age which is characterized by uncertainty and ambiguity considering the magnitude and influence of data-related issues to be solved in contemporary businesses. This study aims to investigate critical success factors that affect business intelligence efficiency based on the DeLone and McLean model in Jordanian banking industry.

A quantitative research method through a questionnaire was used to collect data from actual users who depend on business intelligence tools to make operational and strategic decisions in Jordanian banks. The data obtained were tested using the partial least squares–structural equation modeling approach.

The survey findings attest that system quality, information quality, user quality, user satisfaction and user performance are important factors and contribute to business intelligence efficiency in the Jordanian banking industry.

The findings gained from this work can help policymakers in Jordanian banks to improve the business intelligence success and organizational performance.

To the best of the authors’ knowledge, this study is the first of its kind to propose a theoretical model to assess drivers of BIS efficiency from the Jordanian banks’ perspective.

The overall goal of the data mining process is to extract information from an extensive data set and make it understandable for further use. When working with large volumes of unstructured data in research information systems, it is necessary to divide the information into logical groupings after examining their quality before attempting to analyze it. On the other hand, data quality results are valuable resources for defining quality excellence programs of any information system. Hence, the purpose of this study is to discover and extract knowledge to evaluate and improve data quality in research information systems.

Clustering in data analysis and exploiting the outputs allows practitioners to gain an in-depth and extensive look at their information to form some logical structures based on what they have found. In this study, data extracted from an information system are used in the first stage. Then, the data quality results are classified into an organized structure based on data quality dimension standards. Next, clustering algorithms (K-Means), density-based clustering (density-based spatial clustering of applications with noise [DBSCAN]) and hierarchical clustering (balanced iterative reducing and clustering using hierarchies [BIRCH]) are applied to compare and find the most appropriate clustering algorithms in the research information system.

This paper showed that quality control results of an information system could be categorized through well-known data quality dimensions, including precision, accuracy, completeness, consistency, reputation and timeliness. Furthermore, among different well-known clustering approaches, the BIRCH algorithm of hierarchical clustering methods performs better in data clustering and gives the highest silhouette coefficient value. Next in line is the DBSCAN method, which performs better than the K-Means method.

In the data quality assessment process, the discrepancies identified and the lack of proper classification for inconsistent data have led to unstructured reports, making the statistical analysis of qualitative metadata problems difficult and thus impossible to root out the observed errors. Therefore, in this study, the evaluation results of data quality have been categorized into various data quality dimensions, based on which multiple analyses have been performed in the form of data mining methods.

Although several pieces of research have been conducted to assess data quality results of research information systems, knowledge extraction from obtained data quality scores is a crucial work that has rarely been studied in the literature. Besides, clustering in data quality analysis and exploiting the outputs allows practitioners to gain an in-depth and extensive look at their information to form some logical structures based on what they have found.

Drawing on information foraging theory and the SERVQUAL model, this study built a research model to investigate the roles of middle-aged and elderly short-video creators' online attributes in attracting short-video viewers to be their followers.

Taking Douyin (a famous short-video platform in China) as an example, this study used a sequential triangulation mixed-methods approach (quantitative → qualitative) to examine the proposed model by investigating both creators and viewers.

Viewers who clicked the “like” button for the middle-aged and elderly creators' videos are more likely to follow the creators. Viewers will believe that middle-aged and elderly creators who received more likes are more popular. Thus, middle-aged and elderly creators with more likes usually have more followers. Viewers usually believe that middle-aged and elderly creators who more frequently publish professional and high-quality videos have invested more effort and who have official verification also have a high level of authority and are recognized by the platform. Thus, middle-aged and elderly creators with more professional videos and verification usually have more followers. Moreover, verification, the number of videos and the professionalism of videos can enhance the transformation of viewers who liked middle-aged and elderly creators' videos into their followers, and thus strengthen the positive relationship between the number of likes and the number of followers; however, the number of bio words will have an opposite effect.

These findings have implications for platform managers, middle-aged and elderly creators and the brands aiming to develop a “silver economy” by attracting more followers.

This study researches short-video platforms by using a mixed-methods approach to develop an understanding of viewers' decision-making when following middle-aged and elderly creators based on information foraging theory and the SERVQUAL model from the perspectives of both short-video creators and viewers.