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The purpose of this paper is to examine if university students could accurately extract information from graphs presented in 2D or 3D formats with different colour hue variations or solid black and white.

Participants are presented with 2D and 3D bar and pie charts in a PowerPoint presentation and are asked to extract specific information from the displays. A three (question difficulty) × two (graph type) × two (dimension) × two (colour) repeated measures ANOVA is conducted for both accuracy and reaction time.

Overall, 2D graphs led to better comprehension, particularly when complex information was presented. Accuracy was similar for colour and black and white graphs.

These results suggest that 2D graphs are preferable to 3D graphs, particularly when the task requires that the reader extract complex information.

For the past several decades, diagrams have been valuable additions to textual explanations in textbooks and in the classroom to teach various concepts. With an increase in technological advancements, many authors add extraneous features to their graphs to make them more aesthetically pleasing. This paper has shown, however, that 3D rendering may negatively affect graph comprehension.

This study examines the impact of different Balanced Scorecard (BSC) formats (table, graph without summary measure, graph with a summary measure) on various decision outcomes: performance ratings, perceived informativeness, and decision efficiency.

Using an original case developed by the researchers, a total of 135 individuals participated in the experiment and rated the performance of carwash managers in two different scenarios: one manager excelled financially but failed to meet targets for all other three BSC perspectives and the other manager had the opposite results.

The evaluators rated managerial performance significantly lower in the graph format compared to a table presentation of the BSC. Performance ratings were significantly higher for the scenario where the manager failed to meet only financial perspective targets but exceeded targets for all other nonfinancial BSC perspectives, contrary to the usual predictions based on the financial measure bias. The evaluators reported that informativeness of the BSC was highest in the table or graph without summary measure formats, and, surprisingly, adding a summary measure to the graph format significantly reduced perceived informativeness compared to the table format. Decision efficiency was better for the graph formats (with or without summary measure) than for the table format.

Ours is the first study to compare tables, graphs with and without a summary measure in the context of managerial performance evaluations and to examine their impact on ratings, informativeness, and efficiency. We developed an original case to test the boundaries of the financial measure bias.

The purpose of this paper is to examine the voluntary disclosure of risk-related issues, with a focus on credit risk, in graphical reporting for listed banks in the major European economies. It aims to understand if banks portray credit risk-related information in graphs accurately and whether these graphs provide incremental, rather than replicative, information. It also investigates whether credit risk-related graphs provide a fair representation of risk performance or a more favourable impression than is warranted.

A graphical accuracy index was constructed. Incremental information was measured. A multi-level linear model investigated whether credit risk affects the quantity and quality of graphical credit risk disclosure.

Banks used credit risk graphs to provide incremental information. They were also selective, with riskier banks less likely to use risk graphs. Banks were accurate in their graphical reporting, particularly those with high levels of credit risk. These findings can be explained within an impression management perspective taking human cognitive biases into account. Preparers of risk graphs seem to prefer selective omission over obfuscation via inaccuracy. This probably reflects the fact that individuals, and by implication annual report’s users, generally judge the provision of inaccurate information more harshly than the omission of unfavourable information.

This study provides theoretical insights by pointing out the limitations of a purely economics-based agency theory approach to impression management.

The study suggests annual reports’ readers need to be careful about subtle forms of impression management, such as those exploiting their cognitive bias. Regulatory and professional bodies should develop guidelines to ensure neutral and comparable graphical disclosure.

This study provides a substantive alternative to the predominant economic perspective on impression management in corporate reporting, by incorporating a psychological perspective taking human cognitive biases into account.

The purpose of this paper is to examine the ethical implications of Google's Knowledge Graph. The paper argues that in the advent and implementation of said Knowledge Graph, the role of Google in users' lives and the power held by Google as the key intermediary of information must be scrutinized.

Revisiting existing literature on Google and its impact on knowledge culture, the paper seeks to assess whether the implementation of The Knowledge Graph represents a significant shift in the nature (or use) of the service.

The paper concludes that the extension to Google Search, The Knowledge Graph, can serve to radicalize Google's position as a key intermediary of information in users' lives. Rather than simply serving as a gatekeeper supplying the user with an array of links matching a given query, Google now conveniently disseminates information on their own site, roughly rendering the remainder of the web superfluous. Considering both the commercial nature and the opacity of the service, Google as a de facto solo editor of information is worrying from both a democratic and ethical perspective. A culture of emphatic insistence on convenience and consumption is likely to contribute to the impediment of autonomous information retrieval and digital literacy.

The paper must be considered a preliminary inquiry into Google's reliability as an editor of the body of knowledge. As of yet, no literature specifically has remarked on The Knowledge Graph.

This paper examines whether the newest extension of Google Search, The Knowledge Graph, poses any significant changes to the assessment of the service and its role in the culture. Fostering critical, digital literacy in search engine users is deemed of even more vital importance to society with the implementation of The Knowledge Graph. This paper, preliminary and far from exhaustive, seeks to initiate a discussion on the future responsibilities of Google, scholars and users in securing the ideal of critical digital literacy.

This study surveyed the perceptions of users and preparers of Corporate Annual Reports (CAR) regarding graphical information (GI) disclosed in the CAR. Questionnaires were used and sent to (i) 120 selected users, and (ii) 489 CFO of non‐financial companies listed on the main board of Bursa Malaysia for the year 2002. It was found that users ranked GI as second after financial statements. The users of CAR utilized GI to evaluate company’s performance overtime, make comparison with other companies and assist in making investment decision. The KFV graphs preferred are sales, earnings, EPS, share price performance, and cash flow graphs. More than fifty percent of users believed that graphs disclosed in CAR are sufficient. The survey also revealed that 75.4 percent of Malaysian companies included graphs in their CAR. They disclosed sales, earning per share, shareholders fund, earnings, and net tangible assets. These variables were presented in bar, line, pie, and column. However, the most popular type of graph was bar. The perceived major users of GI are mainly financial analysts, potential investors and financial investors. The preparers indicated that the main reasons that hinder companies from disclosing GI in CAR are due to the sufficiency of the existing numerical and narrative disclosure, and inexistence of formal guideline regarding the construction of graphs. The findings also revealed that users were more aware of fundamental principle of graphs construction than the preparers based on the mean scores by both group on the criterion of good construction of graph. Overall, the survey evidenced that graphs are appreciated by both parties as an alternative way of communicating information in a more effective manner.

Learning from safety accidents and sharing safety knowledge has become an important part of accident prevention and improving construction safety management. Considering the difficulty of reusing unstructured data in the construction industry, the knowledge in it is difficult to be used directly for safety analysis. The purpose of this paper is to explore the construction of construction safety knowledge representation model and safety accident graph through deep learning methods, extract construction safety knowledge entities through BERT-BiLSTM-CRF model and propose a data management model of data–knowledge–services.

The ontology model of knowledge representation of construction safety accidents is constructed by integrating entity relation and logic evolution. Then, the database of safety incidents in the architecture, engineering and construction (AEC) industry is established based on the collected construction safety incident reports and related dispute cases. The construction method of construction safety accident knowledge graph is studied, and the precision of BERT-BiLSTM-CRF algorithm in information extraction is verified through comparative experiments. Finally, a safety accident report is used as an example to construct the AEC domain construction safety accident knowledge graph (AEC-KG), which provides visual query knowledge service and verifies the operability of knowledge management.

The experimental results show that the combined BERT-BiLSTM-CRF algorithm has a precision of 84.52%, a recall of 92.35%, and an F1 value of 88.26% in named entity recognition from the AEC domain database. The construction safety knowledge representation model and safety incident knowledge graph realize knowledge visualization.

The proposed framework provides a new knowledge management approach to improve the safety management of practitioners and also enriches the application scenarios of knowledge graph. On the one hand, it innovatively proposes a data application method and knowledge management method of safety accident report that integrates entity relationship and matter evolution logic. On the other hand, the legal adjudication dimension is innovatively added to the knowledge graph in the construction safety field as the basis for the postincident disposal measures of safety accidents, which provides reference for safety managers' decision-making in all aspects.

The purpose of this paper is to investigate whether analysts’ personal cognitive traits mitigate the efficacy of graphical impression management.

Three experiments are conducted wherein 525 professional accountants working as financial analysts rate a hypothetical company’s performance graph depicting its net income trend. The manipulation is the presence (absence) of impression management techniques. Hypotheses test whether different techniques are effective and whether analysts’ cognitive reflection ability mitigates manipulation efficacy.

Presentation enhancement is effective only with impulsive analysts, showing the weakness of this technique through the use of colors. Measurement distortion and selectivity techniques are effective for reflective and impulsive analysts; however, reflective analysts are more critical about graphs prepared via selectivity that emphasize profit recovery following crises.

Each impression management technique is investigated in isolation and in controlled conditions. Further research could consider how personal cognitive traits impact the efficacy of combined techniques and whether imbedding manipulated graphs with other information mitigates impression management efficacy.

Research on impression management is mostly “task-oriented;” few “people-oriented” studies focus on decision making by those using financial reports. Users’ cognitive reflection ability is shown to undermine the efficacy of some impression management techniques.

Financial analysts, auditors and regulators could develop mechanisms to avoid pervasive usage of (or enhance skepticism regarding) techniques not mitigated by users’ reflectiveness.

Evidence from financial analysts with an accounting background provides insights on individual characteristics’ influence on graphical impression management efficacy.

The purpose of this study is to conduct a comprehensive investigation into the utilization of visual impression management techniques within sustainability reporting. Specifically, the study aims to determine whether Italian companies employ impression management tactics in the presentation of graphs within their sustainability reports and, thus, problematize visual data communication in corporate social responsibility (CSR).

The research adopts a multimodal content analysis of the 58 sustainability reports from Italian listed companies that are GRI-compliant. The analysis focused on three types of graphs: pie charts, line graphs and bar graphs. In total, 860 graphs have been examined.

The study found evidence of graphical distortion techniques being employed by companies in their sustainability reports to create a favorable impression. Specifically, graph distortions are found in column graphs and not in line or pie charts. In particular, selectivity, presentation enhancement and measurement distortion techniques seem to be extensively used when adopting column graphs in sustainability communication. Moreover, social sustainability–related topics tend to be more represented of other area of CSR reporting. This suggests that companies, whether consciously or unconsciously, engage in impression management techniques when using graphs in their sustainability reports.

The study findings suggest that more consciousness is needed for companies when engaging in the construction and selection of graphs in their sustainability reports and that decision-makers should develop a clear guide for ethical visual communication.

The paper systematically analyzes visual impression management techniques in communicating sustainability data and, in particular, advances literature on graphical distortion. The value lies in empirical evidence of distortion adoption in GRI-compliant reports as well as problematizing visual data communication as a fundamental challenge for sustainability communication management.

In recent years, knowledge graph completion has gained increasing research focus and shown significant improvements. However, most existing models only use the structures of knowledge graph triples when obtaining the entity and relationship representations. In contrast, the integration of the entity description and the knowledge graph network structure has been ignored. This paper aims to investigate how to leverage both the entity description and the network structure to enhance the knowledge graph completion with a high generalization ability among different datasets.

The authors propose an entity-description augmented knowledge graph completion model (EDA-KGC), which incorporates the entity description and network structure. It consists of three modules, i.e. representation initialization, deep interaction and reasoning. The representation initialization module utilizes entity descriptions to obtain the pre-trained representation of entities. The deep interaction module acquires the features of the deep interaction between entities and relationships. The reasoning component performs matrix manipulations with the deep interaction feature vector and entity representation matrix, thus obtaining the probability distribution of target entities. The authors conduct intensive experiments on the FB15K, WN18, FB15K-237 and WN18RR data sets to validate the effect of the proposed model.

The experiments demonstrate that the proposed model outperforms the traditional structure-based knowledge graph completion model and the entity-description-enhanced knowledge graph completion model. The experiments also suggest that the model has greater feasibility in different scenarios such as sparse data, dynamic entities and limited training epochs. The study shows that the integration of entity description and network structure can significantly increase the effect of the knowledge graph completion task.

The research has a significant reference for completing the missing information in the knowledge graph and improving the application effect of the knowledge graph in information retrieval, question answering and other fields.

The purpose of this paper is to test the practical utility of attack graph analysis. Attack graphs have been proposed as a viable solution to many problems in computer network security management. After individual vulnerabilities are identified with a vulnerability scanner, an attack graph can relate the individual vulnerabilities to the possibility of an attack and subsequently analyze and predict which privileges attackers could obtain through multi-step attacks (in which multiple vulnerabilities are exploited in sequence).

The attack graph tool, MulVAL, was fed information from the vulnerability scanner Nexpose and network topology information from 8 fictitious organizations containing 199 machines. Two teams of attackers attempted to infiltrate these networks over the course of two days and reported which machines they compromised and which attack paths they attempted to use. Their reports are compared to the predictions of the attack graph analysis.

The prediction accuracy of the attack graph analysis was poor. Attackers were more than three times likely to compromise a host predicted as impossible to compromise compared to a host that was predicted as possible to compromise. Furthermore, 29 per cent of the hosts predicted as impossible to compromise were compromised during the two days. The inaccuracy of the vulnerability scanner and MulVAL’s interpretation of vulnerability information are primary reasons for the poor prediction accuracy.

Although considerable research contributions have been made to the development of attack graphs, and several analysis methods have been proposed using attack graphs, the extant literature does not describe any tests of their accuracy under realistic conditions.