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Fake news stories have become a central element of crises that corporate public relations practitioners have confronted. Whether such stories are rumors, outright lies or deliberate attempts to discredit corporations, they have the same impact and require specific strategies for public relations practitioners to effectively respond. The purpose of this study is to uncover strategies to manage crises that arise from fake news and if and how these strategies differ for other corporate crises.

In this multi-method study of 21 in-depth interviews and a 8-person focus group with senior-level corporate public relations practitioners, authors explored decision-making strategies for responding to fake news crises. Transcripts of interviews and the focus group were thematically analyzed.

Results reveal insights regarding how public relations practitioners determine if and when to respond to fake news crises in corporations; what response strategies public relations practitioners have the autonomy to employ for fake news crises in corporations, and how public relations practitioners control media narratives during fake news crises in corporations.

The findings guide public relations practitioners to craft an autonomous decision-making process and effective online listening strategies—establishing a watchful waiting approach—and determine if the fake news issue is a passing moment or movement swirling into a crisis.

Few studies have examined the perspectives of crisis communication experts about minimizing and managing fake news crises. The study identifies opportunities for future research focused on crises originating from fake news and disinformation.

Effective safety supervision plays a crucial role in ensuring safe production within coal mines. Conventional coal mine safety supervision (CMSS) in China has suffered from the problems of power-seeking, excessive resource consumption and poor timeliness. This paper aims to explore the Internet+ CMSS mode being emerged in China.

The evolution of CMSS systems underwent comprehensive scrutiny through a blend of qualitative and quantitative approaches. First, evolutionary game theory was used to analyze the necessity of incorporating Internet+ technology. Second, a system dynamics model of Internet+ CMSS was crafted, encompassing a system flow diagram and equations for various variables. The model was subsequently simulated by taking the W coal mine in Shanxi Province as a representative case study.

It was revealed that the expected safety profit from the Internet+ mode is 296.03% more than that from the conventional mode. The precise dissemination of law enforcement information was identified as a pivotal approach through which the Internet+ platform served as a conduit to foster synergistic collaboration among diverse elements within the system.

The outcomes of this study not only raise awareness about the potential of Internet+ technology in safety supervision but also establish a vital theoretical foundation for enhancing the efficacy of the Internet+ CMSS mode. The significance of these findings extends to fostering the wholesome and sustainable progress of the coal mining industry.

This research stands out as one of the limited studies that delve into the influence of Internet+ technology on CMSS. Building upon the pivotal approach identified, to the best of authors’ knowledge, a novel “multi-blind” working mechanism for Internet+ CMSS is introduced for the first time.

The COVID-19 pandemic has impacted the construction industry, yet still, it is unclear from existing studies about the critical challenges imposed on quality assurance (QA), particularly Cross-border Construction Logistics and Supply Chain (Cb-CLSC). Thus, this study aims to identify and examine the critical challenges of QA of Cb-CLSC during the COVID-19 pandemic.

The aim is achieved via an embedded mixed-method approach pragmatically involving a desk literature review and engaging 150 experts across the globe using expert surveys, and results confirmed by semi-structured interviews. The approach is based on Interpretive Structural Modelling (ISM) as its foundation.

The study revealed ten critical challenges of QA, with the top four including “the shortage of raw construction material (C7)”, “design changes (C6)”, “collaboration and communication difficulties (C1)” and “changes in work practices (C10)”. However, examining the interrelationships among the critical challenges using ISM confirmed C7 and C10 as the most critical challenges. The study again revealed that the critical challenges are sensitive and capable of affecting themselves due to the nature of their interrelationship based on MICMAC analysis. Hence, being consistent with why all the challenges were considered critical amid the pandemic. Sentiment analysis revealed that the critical challenges have not been entirely negative but also positive by creating three areas of opportunities for improvement: technology adoption, worker management, and work process management. However, four areas of challenges in the QA include cost, raw material, time, and work process, including inspection, testing, auditing, communication, etc.

The finding provides a convenient point of reference to researchers, policymakers, practitioners, and decision-makers on formulating policies to enhance the effectiveness of construction QA during the pandemic through to the post-pandemic era.

The study enriches the extant literature on QA, Cb-CLSC, and the COVID-19 pandemic in the construction industry by identifying the critical challenges and examining the interrelationships among them. This provides a better understanding of how the construction QA has been affected by the pandemic and the opportunities created.

This study aims to empirically investigate the relationship between artificial intelligence (AI) in marketing (AIM) and business performance from the resource-based view (RBV) perspective.

A survey strategy was used in this study to collect data from 225 small and medium enterprises (SMEs) respondents who were on the registered list of the Ghana Enterprise Agency in the Eastern Region of Ghana. Structural equation modeling – path analysis was used to estimate the impact of AIM on the performance of SMEs.

The analyzed data shows that AIM has significant impact on the financial performance, customer performance, internal business process performance and learning and growth performance in the case of SMEs in Ghana. This study establishes the significance of AIM approach in achieving financial performance, customer performance, internal business process performance and learning and growth performance through the application of AIM determinants including, Internet of Things (IoT), collaborative decision-making systems (CDMS), virtual and augmented reality (VAR) and personalization.

Aside the aforementioned significance of this research study, this study has limitations. The sample size of this research study can be expanded to include SME respondents in other geographical areas that were not considered in this study. Future research studies should concentrate on how AIM can analyze customer communications and information such as posts on social media to develop future communications that may enhance customer engagement.

The practical implications comprise of two key items. First, this research study encourages SME owners and managers to develop an AIM method as a fundamental strategic goal in their pursuit to improve SME performance. Second, SME owners and managers should increasingly implement the four determinants of AIM indicated in this research study (i.e., IOT, CDMS, VAR and personalization) to develop essential resources for effective application of AIM to improve their performance.

The results of this study provide a strong support to RBV theory and the proposition that AIM and its determinants (i.e., IOT, CDMS, VAR and personalization) should be recognized as an essential strategic resource for improving the performance (i.e., financial performance, customer performance, internal business process performance and learning and growth performance) of SMEs. This study also contributes to the current body of knowledge on AIM and management, particularly in the context of an emerging economy.

The purpose of this scoping rapid review was to identify and analyse existing qualitative methodologies that have been used to investigate K-12 teachers' lived experiences of adult cyber abuse as a result of student content “going viral” to propose a novel methodological stance incorporating the Australian Online Safety Act 2021.

A search of Google Scholar was conducted using keywords and phrases related to cyber trauma, teachers, qualitative methods and the Online Safety Act. Inclusion criteria for the review were: (1) published in English, (2) focused on teachers' experiences of online abuse and cyberbullying associated with viral posts and (3) employed a qualitative inquiry methodology. Full-text articles were obtained for those that met the inclusion criteria. Data were extracted and analysed using a PRISMA flowchart and inductive thematic analysis.

This methodology is considered to be justified, as the eSafety Commissioner's Safety-by-Design principles do not have any legal or regulatory enforceability, whereas the Online Safety Act 2021 provides the Australian eSafety Commissioner an avenue to drive greater algorithmic transparency and accountability.

The findings of this review informed the development of a novel methodological stance for investigating Australian teachers' lived experiences of adult cyber abuse associated with viral posts. It provides a methodological positioning to support trauma informed qualitative research into adult cyber abuse, informed by the work of the eSafety Commissioner and the Online Safety Act.

Cybertrauma is described as “any trauma that is a result of self- or, other-directed interaction with, mediated through, or from any electronic Internet/cyberspace ready device or machine learning algorithm, that results in impact now or the future” (Knibbs, 2021). It may result from the tracking of movement through various mobile phone features and applications such as location sharing, non-consensual monitoring of social media, and humiliation or punishment through the sharing of intimate images online, through to direct messages of abuse or threats of violence or humiliation. These actions are further perpetuated through automated searches, insights and recommendations on social media (i.e. engagement metrics promote memes, Facebook posts, Tweets, Tiktoks, Youtubes and so on). This is a novel methodology, as it not only considers direct cybertrauma but also automated forms of cybertrauma.

The article investigates different types of strategies for managing user-generated content (UGC) and provides some insights into their implications.

A unique sample of Italian hotels with current and prospective customers in the digital environment is investigated. A taxonomy of user-provider interactions mediated by UGC is developed. A mixed approach was designed to meet the study aims. Firstly, an exploratory factor analysis was performed in order to illuminate different strategies of UGC and electronic word-of-mouth (E-WOM) management. Secondly, a cluster analysis was implemented in order to explain hoteliers' behavior toward users' contents.

The study results suggested the existence of three clusters, which reflected three different types of interactions between hotels and customers in the digital domain. Interestingly, most of Italian hotels were found to adopt a reductionist approach to UGC and E-WOM management, turning out to be ineffective to exploit them for the purpose of quality improvement and hospitality service excellence.

Hotels were found to be largely unaware of the importance of UGC and web-based communication with customers to improve their digital business strategy. Tailored management approaches are needed to realize the full potential of hotels' online content responsiveness for the purpose of value co-creation and service co-production.

This is one of the first studies investigating the strategic and management perspectives embraced by hotels to handle their interactions with customers in the digital arena.

This study aims to establish a model for rapid and accurate emotion recognition in restaurant online reviews, thus advancing the literature and providing practical insights into electronic word-of-mouth management for the industry.

This study elaborates a hybrid model that integrates deep learning (DL) and a sentiment lexicon (SL) and compares it to five other models, including SL, random forest (RF), naïve Bayes, support vector machine (SVM) and a DL model, for the task of emotion recognition in restaurant online reviews. These models are trained and tested using 652,348 online reviews from 548 restaurants.

The hybrid approach performs well for valence-based emotion and discrete emotion recognition and is highly applicable for mining online reviews in a restaurant setting. The performances of SL and RF are inferior when it comes to recognizing discrete emotions. The DL method and SVM can perform satisfactorily in the valence-based emotion recognition.

These findings provide methodological and theoretical implications; thus, they advance the current state of knowledge on emotion recognition in restaurant online reviews. The results also provide practical insights into intelligent service quality monitoring and electronic word-of-mouth management for the industry.

This study proposes a superior model for emotion recognition in restaurant online reviews. The methodological framework and steps are elucidated in detail for future research and practical application. This study also details the performances of other commonly used models to support the selection of methods in research and practical applications.

This paper aims to present a literature review of remote monitoring systems for water infrastructure in the Global South.

Following initial scoping searches, further examination was made of key remote monitoring technologies for water infrastructure in the Global South. A standard literature search methodology was adopted to examine these monitoring technologies and their respective deployments. This hierarchical approach prioritised “peer-reviewed” articles, followed by “scholarly” publications, then “credible” information sources and, finally, “other” relevant materials. The first two search phases were conducted using academic search services (e.g. Scopus and Google Scholar). In the third and fourth phases, Web searches were carried out on various stakeholders, including manufacturers, governmental agencies and non-governmental organisations/charities associated with Water, Sanitation and Hygiene (WASH) in the Global South.

This exercise expands the number of monitoring technologies considered in comparison to earlier review publications. Similarly, preceding reviews have largely focused upon monitoring applications in sub-Saharan Africa (SSA). This paper explores opportunities in other geographical regions and highlights India as a significant potential market for these tools.

This review predominantly focuses upon information/data currently available in the public domain.

Remote monitoring technologies enable the rapid detection of broken water pumps. Broken water infrastructure significantly impacts many vulnerable communities, often leading to the use of less protected water sources and increased exposure to water-related diseases. Further to these public health impacts, there are additional economic disadvantages for these user communities.

This literature review has sought to address some key technological omissions and to widen the geographical scope associated with previous investigations.

Taking into consideration the current human need for agricultural produce such as rice that requires water for growth, the optimal consumption of this valuable liquid is important. Unfortunately, the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption. Therefore, designing and implementing a mechanized irrigation system is of the highest importance. This system includes hardware equipment such as liquid altimeter sensors, valves and pumps which have a failure phenomenon as an integral part, causing faults in the system. Naturally, these faults occur at probable time intervals, and the probability function with exponential distribution is used to simulate this interval. Thus, before the implementation of such high-cost systems, its evaluation is essential during the design phase.

The proposed approach included two main steps: offline and online. The offline phase included the simulation of the studied system (i.e. the irrigation system of paddy fields) and the acquisition of a data set for training machine learning algorithms such as decision trees to detect, locate (classification) and evaluate faults. In the online phase, C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.

The proposed approach is a comprehensive online component-oriented method, which is a combination of supervised machine learning methods to investigate system faults. Each of these methods is considered a component determined by the dimensions and complexity of the case study (to discover, classify and evaluate fault tolerance). These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods. As a result, depending on the conditions under study, the most efficient method is selected in the components. Before the system implementation phase, its reliability is checked by evaluating the predicted faults (in the system design phase). Therefore, this approach avoids the construction of a high-risk system. Compared to existing methods, the proposed approach is more comprehensive and has greater flexibility.

By expanding the dimensions of the problem, the model verification space grows exponentially using automata.

Unlike the existing methods that only examine one or two aspects of fault analysis such as fault detection, classification and fault-tolerance evaluation, this paper proposes a comprehensive process-oriented approach that investigates all three aspects of fault analysis concurrently.

The different dimensions of the online engagement behaviors exhibited by omnichannel shoppers, who mainly rely on the online channel for information search, are still understudied. This study aims to investigate how service journey quality (SJQ) has an impact on the overall omnichannel customer experience leading to customer identification (CI) with the store, subsequently leading to their exhibition of online engagement behaviors (writing online reviews, blogging, rating products and service online and indulging in customer-to-customer online interactions.

The research is cross-sectional, quantitative and descriptive. Purposive sampling was used to choose the research's participants. Data were collected from 591 Indian omnichannel customers who had previously made an omnichannel purchase that included the concurrent usage of various channels of a retailer using a verified self-administered survey. Using the Smart PLS 4.0 software, the proposed conceptual model has been evaluated.

The results indicate that omnichannel customer experience mediates the relationship between SJQ and CI with the store, subsequently leading to their exhibition of online engagement behaviors (writing online reviews, blogging, rating products and service online and indulging in customer-to-customer online interactions). The perceived customer gratitude toward the store significantly and positively moderated the direct relationship between SJQ and different online engagement behaviors (writing online reviews, blogging, rating products and service online and indulging in customer-to-customer online interactions).

The study relied upon the omnichannel shoppers of only Indian population and relied on a cross-sectional data collection procedure for this research.

Post-pandemic, with highly dynamic shifts in customer preferences, the need for channel-agnostic shopping leading to the unpredictability of purchase patterns has made SJQ the only dimension to achieve sustainable loyalty intentions through value co-creation in an omnichannel retail context. Emphasizing post-purchase behaviors like different online engagement behaviors (writing online reviews, blogging, rating products and services online and indulging in customer-to-customer online interactions), this study is the first to show that SJQ might affect four different online customer engagement behaviors through omnichannel shopping experience and CI with the store. The moderating effect of customer-perceived gratitude toward the retailer on a few proposed hypotheses was also tested to give managerial recommendations. The study also answers the call to investigate the moderating role of customer gratitude in determining service quality-driven engagement behaviors.