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This paper aims to unify fragmented definitions of fake news and also present a comprehensive classification of the concept. Additionally, it provides an agenda for future marketing research based on the findings.

A review of 36 articles investigating fake news from 1990 to 2020 was done. In total, 615 papers were found, and the article pool was refined manually in two steps; first, articles were skimmed and scanned for nonrelated articles; second, the pool was refined based on the scope of the research.

The review resulted in a new definition and a collective classification of fake news. Also, the feature of each type of fake news, such as facticity, intention, harm and humor, is examined as well, and a definition for each type is presented.

This extensive study, to the best of the author’s knowledge, for the first time, reviews major definitions and classification on fake news.

Este artículo pretende unificar las definiciones fragmentadas de las noticias falsas y también presentar una clasificación exhaustiva del concepto. Además, ofrece una agenda para futuras investigaciones de marketing basada en los resultados.

Se realizó una revisión de 36 artículos que investigaban las noticias falsas desde 1990 hasta 2020. Se encontraron 615 artículos, y el grupo de artículos se refinó manualmente en dos pasos, primero, se descremaron los artículos y se escanearon los artículos no relacionados, segundo, el grupo se refinó basado en el alcance de la investigación.

La revisión dio como resultado una nueva definición y una clasificación colectiva de las noticias falsas. Además, se examinan las características de cada tipo de noticias falsas, como la facticidad, la intención, el daño y el humor, y se presenta una definición para cada tipo.

este amplio estudio revisa por primera vez las principales definiciones y la clasificación de las noticias falsas.

本文旨在统一假新闻的零散定义, 并对假新闻的概念进行全面的分类。此外, 它还根据本文的研究结果为未来的营销研究提供了一个议程。

对1990年至2020年期间调查假新闻的36篇文章进行了回顾。一共发现了615篇论文, 并分为两步对此文章库进行了人工提炼：首先, 对文章进行略读和扫描以找出非相关文章, 其次, 根据研究范围对文章库进行了提炼。

此次审查导致了对假新闻的新定义和集体分类。此外, 还分析了假新闻的真实性、意图、危害性、幽默性等各种类型的特征, 并给出了各种类型的定义。

此项涉及广泛假新闻内容的研究首次回顾了关于假新闻的主要定义和分类。

This paper aims to conduct an interdisciplinary systematic literature review (SLR) of fake news research and to advance the socio-technical understanding of digital information practices and platforms in business and management studies.

The paper applies a focused, SLR method to analyze articles on fake news in business and management journals from 2010 to 2020.

The paper analyzes the definition, theoretical frameworks, methods and research gaps of fake news in the business and management domains. It also identifies some promising research opportunities for future scholars.

The paper offers practical implications for various stakeholders who are affected by or involved in fake news dissemination, such as brands, consumers and policymakers. It provides recommendations to cope with the challenges and risks of fake news.

The paper discusses the social consequences and future threats of fake news, especially in relation to social networking and social media. It calls for more awareness and responsibility from online communities to prevent and combat fake news.

The paper contributes to the literature on information management by showing the importance and consequences of fake news sharing for societies. It is among the frontier systematic reviews in the field that covers studies from different disciplines and focuses on business and management studies.

This conceptual paper aims to explore the current state of sustainability communication research, focusing on the challenges of communicating inconvenient truths in an era of scientific mistrust. Therefore, this study aims to (1) examine the existing research landscape in sustainability communication, (2) identify unresolved problems and challenges, and (3) propose strategies for counteract misinformation through targeted communication.

For this, the authors conducted a critical literature review and analyzed the resulting sample (n = 473 journal articles) by means of qualitative content analysis to (1) evaluate existing communication approaches dealing with the communication of sustainability's inconvenient truth, (2) identify stakeholder groups involved in sustainability communication, (3) discuss limitations of current communication approaches and (4) present recommendations on (more) effective communication strategies to address the unresolved issues in sustainability communication.

The analysis reveals that when it comes to sustainability communication and its unresolved problems, literature refers to four key stakeholder groups: (1) science deniers; (2) adaptation skeptics; (3) whitewashers and (4) world saviors. Furthermore, the analysis provides valuable insights into the complex dynamics involved in communicating sustainability, emphasizes the need for tailored approaches to engage and address the concerns of each stakeholder group, and exposes limitations in current communication methods and approaches. Accordingly, the analysis highlights the necessity of developing new theories, models and methods specific to sustainability communication to tackle its unique challenges effectively.

Like our society, communication sciences need a fundamental transformation to meet sustainability communication's new challenges induced by the necessary shift toward sustainable development.

This paper provides a comprehensive overview of the current state of sustainability communication in research, specifically addressing the challenges of effectively communicating unpleasant news in the context of scientific mistrust. It fills a gap in existing literature by examining the progress made in addressing these issues and identifying the emerging challenges that need to be addressed.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

The current study is aimed at identifying the prominent influencers that affect the response behaviour of patients in a hospital environment.

The research is based on the data collected through the participant observation method while interviewing patients about the quality of healthcare services in nine community health centres of the Kashmir division. Thematic analysis was performed on the information collected from patients admitted to various hospital sections.

The analysis of the qualitative data revealed that the presence of hospital staff near respondents, perceived risk of maltreatment, social desirability, the sensitivity of the topic, risk of information sharing and attitude towards surveys are the most frequently observed factors that modulate the patient's tendency to truthfully report critical facts about the problem understudy.

These results can help researchers to exercise caution while communicating with respondents and collecting data related to serious issues in a natural setting.

This study aims to introduce a new methodology for generating synthetic images for facility management purposes. The method starts by leveraging the existing 3D open-source BIM models and using them inside a graphic engine to produce a photorealistic representation of indoor spaces enriched with facility-related objects. The virtual environment creates several images by changing lighting conditions, camera poses or material. Moreover, the created images are labeled and ready to be trained in the model.

This paper focuses on the challenges characterizing object detection models to enrich digital twins with facility management-related information. The automatic detection of small objects, such as sockets, power plugs, etc., requires big, labeled data sets that are costly and time-consuming to create. This study proposes a solution based on existing 3D BIM models to produce quick and automatically labeled synthetic images.

The paper presents a conceptual model for creating synthetic images to increase the performance in training object detection models for facility management. The results show that virtually generated images, rather than an alternative to real images, are a powerful tool for integrating existing data sets. In other words, while a base of real images is still needed, introducing synthetic images helps augment the model’s performance and robustness in covering different types of objects.

This study introduced the first pipeline for creating synthetic images for facility management. Moreover, this paper validates this pipeline by proposing a case study where the performance of object detection models trained on real data or a combination of real and synthetic images are compared.

The purpose of this paper is to present a framework of ideation pathways that organically extend the current stock of knowledge to generate new and useful knowledge. Although detailed, granular guidance is available in the strategy literature on all aspects of empirically testing theory, the other key aspect of theory development – theory generation – remains relatively neglected. The framework developed in this paper addresses this gap by proposing pathways for how new theory can be generated.

Grounded in two foundational principles in epistemology, the Genetic Argument and the open-endedness of knowledge, I offer a framework of distinct pathways that systematically lead to the creation of new knowledge.

Existing knowledge can be deepened (through introspection), broadened (through leverage) and rejuvenated (through innovation). These ideation pathways can unlock the vast, hidden potential of current knowledge in strategy.

The novelty and doability of the framework can potentially inspire research on a broad, community-wide basis, engaging PhD students and management faculty, improving knowledge, democratizing scholarship and deepening the societal footprint of strategy research.

Knowledge is open-ended. The more we know, the more we appreciate how much we don’t know. But the lack of clear guidance on rigorous pathways along which new knowledge that advances both theory and practice can be created from prior knowledge has stymied strategy research. The paper’s framework systematically pulls together for the first time the disparate elements of transforming past learning into new knowledge in a coherent epistemological whole.

This study aims to contribute to the field of customer portfolio management by proposing a novel approach rooted in dialectic critical realism (DCR). DCR, as an ontological theory, enables a fundamental reimagining of customer portfolio management as a dialectic process. The conceptualized dialectic portfolio management is motivated by the concept of “absence”, akin to Hegelian “antithesis”, which highlights limitations, problems and tensions in portfolio management. In essence, “absence” serves as a diagnostic tool that directs portfolio actions towards resolving problems by pursuing a more comprehensive “totality”, similar to the Hegelian notion of “synthesis”.

This conceptual paper theorizes DCR in business marketing and customer portfolio management.

DCR conceptualizes customer portfolios as relational structures characterized by omissions and tensions. These issues are addressed through a dialectic synthesis aimed at achieving a more comprehensive “totality”. Consequently, DCR guides portfolio management to continually re-think the connections and distinctions that define a portfolio within its network context. This dialectic process is facilitated by a novel vocabulary that enhances the understanding of network and portfolio relations, incorporating concepts such as “intrapermeations”, “existential constitutions”, “intra-connections” and “intensive” and “extensive” portfolio practices.

This study aims to foster a fresh and process-oriented perspective on portfolio management, drawing inspiration from the growing demand for enriched dialectic theorizing within the realm of business marketing. The adoption of a dialectic process orientation based on DCR revolutionizes the comprehension of portfolio management by fundamentally reimagining the underlying ontological assumptions that underpin the existing body of literature on customer portfolios. Moreover, DCR asserts that ethical considerations are inextricably linked to human experiences and associated practices, emphasizing ethics as an integral component of customer portfolio management.

This study answers the call for research and theorising exploring ethical communication and brand risk from the African continent. The study's purpose was to identify the challenges that strategic communication practitioners face in enacting ethical crisis communication in South Africa.

The researchers conducted ten in-depth interviews with South African strategic communication professionals.

The dominant theme emerging from the study is the marginalisation and exclusion of the communication function in decision-making during crisis situations. Communicators were viewed as implementers, technicians and not strategic counsel. The protection of organisational reputation was done at the expense of the ethics and moral conscience of practitioners. Practitioners were viewed and deployed as spin doctors and tools to face unwanted media interactions.

The article sheds light on the concepts of ethical communication and decision-making in a multicultural African context using the moral theory of Ubuntu and strategic communication. It demonstrates the tension professionals experience as they toggle between unethical capitalist approaches and African values. The practitioner's role as organisational moral conscience is hindered, suppressed and undermined by organisational leadership's directives to use opaque, complex communication, selective transparency and misrepresentation of facts.

This paper examines the theoretical issues and research themes of business and management impact. Our empirical setting is the UK Research Excellence Framework 2014 (REF 2014) and the focus is on the nature of research impact. Stakeholders, including Governments, now expect academic outputs to translate to real world benefits beyond the narrow bibliometric type metrics.

Despite decades of academic literature devoted to business and management research impact, current theories cannot explain the apparent disconnect between academic, economic and societal practice. Adopting a UK Business and Management perspective to frame our investigation, we consider the highly contested rhetorical question – What are the current themes and impacts of Business and Management research?

We propose a definition for research impact and consider its measurement. Then, using the 410 Impact Case Studies submitted to REF 2014 #x2013; Unit of Assessment 19, business and management, we examine how high impact unfolds. The implications for business and management research impact from the perspectives of economic, knowledge and responsibility impacts are considered.