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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.

This paper aims to review and critically assess the role that data visualizations played as communication media tools to help society during a worldwide crisis. This paper re-creates and analyzes several visualizations, critically and ethically assesses their strengths and limitations and provides a set of best practices that are informative, accurate, ethical and engaging at each stage in a reader’s interest.

The paper bases its methodology on the construct of “The Network Society” (Van Dijk, 2006; Castells, 2000, 2006) by creating a series of social networked visualizations, identifying the challenges and pitfalls associated with this communication approach and suggesting best practices in information communication technology. The case study is COVID-19.

The research in this study found that visual data dashboards and interactive Web-based charts did play a significant role in helping society understand COVID-19’s impact to make better informed decisions about society’s health and safety.

Visual expositions of data do have strengths and weaknesses depending on how they are designed, how they communicate the story and how they are ethically deployed. Best practices are provided to help mitigate these limitations.

Visualizations are certainly not new, but the technology for rapidly developing and sharing them is new. Visual expositions provide an effective media for communicating complex information to a networked society.

Visual expositions provide an effective media for communicating complex information to a networked society.

This paper highlights the significance of the need to understand complex data in a crisis in a visual format and to communicate the information quickly, persuasively, effectively and ethically to a networked audience.

Entrepreneurial identity is a complex concept. It has been recognised as a subjective and dynamic socio-cognitive factor which is not stable over time and is part of an iterative formation process. This chapter explores the journey of adopting, implementing and reviewing visual methods, in order to examine entrepreneurial identity, from the researchers’ perspectives. A critical standpoint is offered which explores both the benefits and challenges that presented themselves in the search for rich data.

Docking technology plays a crucial role in enabling long-duration operations of autonomous underwater vehicles (AUVs). Visual positioning solutions alone are susceptible to abnormal drift values due to the challenging underwater optical imaging environment. When an AUV approaches the docking station, the absolute positioning method fails if the AUV captures an insufficient number of tracers. This study aims to to provide a more stable absolute position visual positioning method for underwater terminal visual docking.

This paper presents a six-degree-of-freedom positioning method for AUV terminal visual docking, which uses lights and triangle codes. The authors use an extended Kalman filter to fuse the visual calculation results with inertial measurement unit data. Moreover, this paper proposes a triangle code recognition and positioning algorithm.

The authors conducted a simulation experiment to compare the underwater positioning performance of triangle codes, AprilTag and Aruco. The results demonstrate that the implemented triangular code reduces the running time by over 70% compared to the other two codes, and also exhibits a longer recognition distance in turbid environments. Subsequent experiments were carried out in Qingjiang Lake, Hubei Province, China, which further confirmed the effectiveness of the proposed positioning algorithm.

This fusion approach effectively mitigates abnormal drift errors stemming from visual positioning and cumulative errors resulting from inertial navigation. The authors also propose a triangle code recognition and positioning algorithm as a supplementary approach to overcome the limitations of tracer light positioning beacons.

This paper presents a survey of research into interactive robotic systems for the purpose of identifying the state of the art capabilities as well as the extant gaps in this emerging field. Communication is multimodal. Multimodality is a representation of many modes chosen from rhetorical aspects for its communication potentials. The author seeks to define the available automation capabilities in communication using multimodalities that will support a proposed Interactive Robot System (IRS) as an AI mounted robotic platform to advance the speed and quality of military operational and tactical decision making.

This review will begin by presenting key developments in the robotic interaction field with the objective of identifying essential technological developments that set conditions for robotic platforms to function autonomously. After surveying the key aspects in Human Robot Interaction (HRI), Unmanned Autonomous System (UAS), visualization, Virtual Environment (VE) and prediction, the paper then proceeds to describe the gaps in the application areas that will require extension and integration to enable the prototyping of the IRS. A brief examination of other work in HRI-related fields concludes with a recapitulation of the IRS challenge that will set conditions for future success.

Using insights from a balanced cross section of sources from the government, academic, and commercial entities that contribute to HRI a multimodal IRS in military communication is introduced. Multimodal IRS (MIRS) in military communication has yet to be deployed.

Multimodal robotic interface for the MIRS is an interdisciplinary endeavour. This is not realistic that one can comprehend all expert and related knowledge and skills to design and develop such multimodal interactive robotic interface. In this brief preliminary survey, the author has discussed extant AI, robotics, NLP, CV, VDM, and VE applications that is directly related to multimodal interaction. Each mode of this multimodal communication is an active research area. Multimodal human/military robot communication is the ultimate goal of this research.

A multimodal autonomous robot in military communication using speech, images, gestures, VST and VE has yet to be deployed. Autonomous multimodal communication is expected to open wider possibilities for all armed forces. Given the density of the land domain, the army is in a position to exploit the opportunities for human–machine teaming (HMT) exposure. Naval and air forces will adopt platform specific suites for specially selected operators to integrate with and leverage this emerging technology. The possession of a flexible communications means that readily adapts to virtual training will enhance planning and mission rehearsals tremendously.

Interaction, perception, cognition and visualization based multimodal communication system is yet missing. Options to communicate, express and convey information in HMT setting with multiple options, suggestions and recommendations will certainly enhance military communication, strength, engagement, security, cognition, perception as well as the ability to act confidently for a successful mission.

The objective is to develop a multimodal autonomous interactive robot for military communications. This survey reports the state of the art, what exists and what is missing, what can be done and possibilities of extension that support the military in maintaining effective communication using multimodalities. There are some separate ongoing progresses, such as in machine-enabled speech, image recognition, tracking, visualizations for situational awareness, and virtual environments. At this time, there is no integrated approach for multimodal human robot interaction that proposes a flexible and agile communication. The report briefly introduces the research proposal about multimodal interactive robot in military communication.

It is widely known that fast-fashion retailers are struggling to keep up with consumer attention for quick responses within the fashion industry. With the advance of Internet and e-commerce, consumers prefer to purchase online. Online platform information has become an essential source for exploring consumer attention. However, there is often a mismatch between the information provided by retailers and the feedback received from consumers, leading to an imbalance between the supply side and demand side of online information. The purpose of this study is therefore to provide a unified approach to discover consumer attention from the design topic aspect by revealing the information imbalance between supply side and demand side.

To address the issue of online information imbalance and discover consumer attention, this study proposed an approach that focuses on the design topic perspective. The design topic is a collection of design elements that represent a clothing-design feature more comprehensively and accurately compared to a single design element. The proposed approach begins with generating design topics through topic modeling based on online information provided by retailers on e-commerce platforms. Two indicators, influence degree and attention degree, are then used to quantify the intensity of supply information and consumer attention related to design topics. Finally, design topic strategy diagrams are constructed to reveal information imbalance and discover consumer attention.

The experimental case demonstrates the existence of information imbalance, indicating that the intensity of supply information and consumer attention from the perspective of design topics is not uniform, although both follow the Pareto principle. The results of consumer attention distribution with heavy power-law tails are consistent with current research findings. This further demonstrates that the proposed approach is capable of discovering consumer attention in the design topic strategy diagrams.

The issue of information imbalance between retailers and consumers poses a challenge in keeping up with customer attention. The proposed approach offers a practical solution by visually identifying the symptoms of information imbalance and discovering consumer attention through design topic strategy diagrams. This approach provides fast-fashion retailers with a valuable reference to seize market opportunities, improve product design and adjust marketing or management strategies.

This study proposes a novel approach to disclose the issue of information imbalance between supply side and demand side and therefore to discover consumer attention from the perspective of design topics. In addition, guidelines for applying the proposed approach for fast-fashion marketing and management are presented.

The aim of the study is threefold: understanding the interconnections amongst visual and verbal multimodal communication strategies used in food discourse; identifying the themes of celebrity chef's food discourse with respect to pro-environmental behaviour; and providing a methodological framework to visually analyse food-themed videos.

This study uses mise-en-scène and critical discourse and multimodal analyses to gain insights on food discourse from 20 videos shared by a Michelin starred chef on social media platforms.

Results show that a pro-environmental cooking philosophy challenges the normative discourse on food and educates general audiences and foodies alike. Mise-en-scène and discourse analyses of Instagram visual content reveal that leftovers are central to the ethical message and are intertwined – through the aesthetic of the videos-with concepts of inclusivity, diversity and nourishment.

Chefs, and restaurants, are encouraged to recognise their responsibility as role models, thus able to influence the societal production of food discourse.

The findings provide new insights into the role of a celebrity chef in promoting sustainable food preparation and consumption.

This study aims to explore how a postmodern tribe enacts and re-interprets ideologies as a part of consumers’ collective experience, to enhance our understanding of consumer communities in conjunction with ideological capacities.

The community of “presenteers” is conceptualized as a self-organized tribe with heterogeneous components that generate capacities to act. Netnographic observation was conducted on 18 presenteer accounts and lasted around six months. Real-time data were collected by taking screenshots of the posts and stories that these users created and publicly shared. Data were analysed by adopting assemblage theory, combining inductive and deductive approaches. Firstly, a qualitative visual-textual content analysis of the tribe’s defining components was conducted. Then, the process continued with the thematic analysis of the ideological underpinnings of the tribe’s enactments.

Findings shed light on the ways in which consumer communities interpret the entanglement of religious, political, and cultural ideologies in shaping their experiences. In the case of the presenteers tribe, findings reflect a novel ideological interplay between neo-Ottomanism, post-feminism and consumerism.

The study offers a deep dive into a unique tribe that is being organized around the consumer-created practice of “presenteering” and investigates consumer communalization in alignment with the ideological turn in culture-oriented interpretative research on consumers, consumption, and markets. This exploration helps to bridge the research on the communalization of consumers with the recent discussions of ideology in the postmodern market.

The study offers a deep dive into a unique tribe that is being organized around the consumer-created practice of “presenteering” and investigates consumer communalization in alignment with the ideological turn in culture-oriented interpretative research on consumers, consumption, and markets. This exploration helps to bridge the research on the communalization of consumers with the recent discussions of ideology in the postmodern market.

This research investigates the critical role of data governance (DG) in shaping a data-driven culture (DDC) within organizations, recognizing the transformative potential of data utilization for efficiency, opportunities, and productivity. The study delves into the influence of DG on DDC, emphasizing the mediating effect of data literacy (DL).

The study empirically assesses 125 experienced managers in Indonesian public service sector organizations using a quantitative approach. Structural Equation Modeling (SEM) analysis was chosen to examine the impact of DG on DDC and the mediating effects of DL on this relationship.

The findings highlight that both DG and DL serve as antecedents to DDC, with DL identified as a crucial mediator, explaining a significant portion of the effects between DG and DDC.

Beyond unveiling these relationships, the study discusses practical implications for organizational leaders and managers, emphasizing the need for effective policies and strategies in data-driven decision-making.

This research fills an important research gap by introducing an original model and providing empirical evidence on the dynamic interplay between DG, DL, and DDC, contributing to the evolving landscape of data-driven organizational cultures.

The purpose of this study is to explore to which extent data mining research would be associated with the library and information science (LIS) discipline. This study aims to identify data mining related subject terms and topics in representative LIS scholarly publications.

A large set of bibliographic records over 38,000 was collected from a scholarly database representing the fields of LIS and the data mining, respectively. A multitude of text mining techniques were applied to investigate prevailing subject terms and research topics, such as influential term analysis and Dirichlet multinomial regression topic modeling.

The findings of this study revealed the relationship between the LIS and data mining research domains. Various data mining method terms were observed in recent LIS publications, such as machine learning, artificial intelligence and neural networks. The topic modeling result identified prevailing data mining related research topics in LIS, such as machine learning, deep learning, big data and among others. In addition, this study investigated the trends of popular topics in LIS over time in the recent decade.

This investigation is one of a few studies that empirically investigated the relationships between the LIS and data mining research domains. Multiple text mining techniques were employed to delineate to which extent the two research domains would be associated with each other based on both at the term-level and topic-level analysis. Methodologically, the study identified influential terms in each domain using multiple feature selection indices. In addition, Dirichlet multinomial regression was applied to explore LIS topics in relation to data mining.