Explore the vast range of titles available.

Online health platforms are currently experiencing significant growth. Patients can conveniently seek medical consulting services on such platforms. Against the backdrop of the thriving development of digital health care, the patterns of physician-patient communication are undergoing profound changes. It is imperative to focus on physician discourse strategies during online physician-patient interactions, which will improve the efficiency of physician-patient communication and achieve better management of the physician-patient relationship. This study aims to explore the influencing mechanism between physician discourse strategies and patient consultation behavior on online health platforms. Additionally, we explore the crucial mediating role of online physician-patient trust and the moderating role of shared decision-making in the online physician-patient communication process. We used a mixed research approach to explore the influencing mechanism. Data on physician basic attributes and physician-patient communication text records were collected from the Chunyu Doctor website using a web spider. The study obtained a total of 8628 interaction texts from January 2022 to July 2023. Physician discourse strategies (capacity-oriented strategy, quality-oriented strategy, and goodwill-oriented strategy), online physician-patient trust, and shared decision-making were captured through text mining and a random forest model. First, we employed text mining to extract the speech acts, modal resources, and special linguistic resources of each record. Then, using a well-trained random forest model, we captured the specific discourse strategy of each interaction text based on the learned features and patterns. The study generated 863 groups of physician samples with 17 data fields. The hypotheses were tested using an \"ordinary least squares\" model, and a stability test was conducted by replacing the dependent variable. The capacity-oriented strategy, goodwill-oriented strategy, and quality-oriented strategy had significant effects on patient consultation behavior (β=.151, P=.007; β=.154, P<.001; and β=.17, P<.001, respectively). It should be noted that the anticipated strong effect of the capacity-oriented strategy on patient consultation behavior was not observed. Instead, the effects of the quality-oriented strategy and goodwill-oriented strategy were more prominent. Physician notification adequacy from shared decision-making moderated the effect between the goodwill-oriented strategy and patient consultation behavior (β=.172; P<.001). Additionally, patient expression adequacy from shared decision-making moderated the effect between the capacity-oriented strategy and patient consultation behavior (β=.124; P<.001), and between the goodwill-oriented strategy and patient consultation behavior (β=.104; P=.003). Online physician-patient trust played a significant mediating role between physician discourse strategies and patient consultation behavior. The study findings suggest significant implications for stimulating patient consultation behavior on online health platforms by providing guidance on effective discourse strategies for physicians, thus constructing a trustworthy physician image, improving the physician-patient relationship, and increasing platform traffic.

This paper uses multilevel discourse analysis to advance strategic management research, focusing on the question of why firms fail in the face of radical technological change. Answering this question requires addressing how customers develop their interpretations and evaluation criteria of the new technology. This interpretive process occurs through discussions with other market participants. Firms can influence customers' interpretations through the use of language and visual images—what we call \"discursive strategies.\" Firms can fail to navigate technological disruptions because their discursive strategies do not communicate effectively with customers. Yet, methodological limitations have restricted the study of discursive strategies. We draw on multilevel discourse analysis and apply this method to explain why IBM outperformed Remington Rand in the early insurance market for computers.

The present paper brings to the fore issues relating to the meaning and construction of ethics in online team communication by exploring the discursive strategies that contribute to the construction of a team’s sense of duty and individual virtuousness. The study relies on a complex toolkit which includes ethnolinguistics, sociolinguistics, discourse and conversation analysis. Data consist of a one-day interaction unit as part of a larger set of real communication exchanges (ca. 34,000) over a time period of six months, observation notes, as well as unstructured interviews. Our empirical analysis has revealed that individual virtuousness and sense of duty are actually interrelated. A virtuous team climate leads team members to share positive perceptions about the team, which in turn increases team commitment. Furthermore, we argue that the blurring of private and professional life not only allows for the enactment of ethic-driven discourse strategies that result in enhanced cooperation and improved team performance, but also for high levels of interconnectivity and improved social interaction. The results of the analysis supplement organisational literature based on ethics-centred observations on the effectiveness of virtual work, and show how a discourse-driven approach can provide tools for further theorisations about the practices and the ecology of digital communication.

Using Donald Trump’s presidential campaign speeches from 2016 as its corpus, this paper takes Talmy’s (1988) force-dynamics theory as a framework for deconstructing and analyzing political discourse from a cognitive perspective. In view of Newton’s second law of classical mechanics, this study analyzes the source and composition of social force within force-dynamics theory and constructs a cognitive model of discourse: F (social force) = m (quantification of discourse information) * a (discourse strategy), applicable to analysis of the relations among language, rights, and ideology in political discourse. In Trump’s campaign speeches, the quantification of discourse information and strategies using cognitive mechanisms such as metaphor, metonymy, and categorization greatly strengthens the social force of the discourse. This plays an important role in conveying Trump’s intention to voters, influencing the public’s emotions and winning their support—thus changing the original psychological state of voters and finally even influencing the election result.

Several countries have successfully reduced their COVID-19 infection rate early, while others have been overwhelmed. The reasons for the differences are complex, but response efficacy has in part depended on the speed and scale of governmental intervention and how communities have received, perceived, and acted on the information provided by governments and other agencies. While there is no ‘one size fits all’ communications strategy to deliver information during a prolonged crisis, in this article, we draw on key findings from scholarship in multiple social science disciplines to highlight some fundamental characteristics of effective governmental crisis communication. We then present ten recommendations for effective communication strategies to engender maximum support and participation. We argue that an effective communication strategy is a two-way process that involves clear messages, delivered via appropriate platforms, tailored for diverse audiences, and shared by trusted people. Ultimately, the long-term success depends on developing and maintaining public trust. We outline how government policymakers can engender widespread public support and participation through increased and ongoing community engagement. We argue that a diversity of community groups must be included in engagement activities. We also highlight the implications of emerging digital technologies in communication and engagement activities.

While organizations today make extensive use of complex algorithms, the notion of algorithmic accountability remains an elusive ideal due to the opacity and fluidity of algorithms. In this article, we develop a framework for managing algorithmic accountability that highlights three interrelated dimensions: reputational concerns, engagement strategies, and discourse principles. The framework clarifies (a) that accountability processes for algorithms are driven by reputational concerns about the epistemic setup, opacity, and outcomes of algorithms; (b) that the way in which organizations practically engage with emergent expectations about algorithms may be manipulative, adaptive, or moral; and (c) that when accountability relationships are heavily burdened by the opacity and fluidity of complex algorithmic systems, the emphasis of engagement should shift to a rational communication process through which a continuous and tentative assessment of the development, workings, and consequences of algorithms can be achieved over time. The degree to which such engagement is, in fact, rational can be assessed based on four discourse-ethical principles of participation, comprehension, multivocality, and responsiveness. We conclude that the framework may help organizations and their environments to jointly work toward greater accountability for complex algorithms. It may further help organizations in reputational positioning surrounding accountability issues. The discourse-ethical principles introduced in this article are meant to elevate these positioning contests to extend beyond mere adaption or compliance and help guide organizations to find moral and forward-looking solutions to accountability issues.

The energy products of oil and gas majors have contributed significantly to global greenhouse gas emissions (GHG) and planetary warming over the past century. Decarbonizing the global economy by mid-century to avoid dangerous climate change thus cannot occur without a profound transformation of their fossil fuel-based business models. Recently, several majors are increasingly discussing clean energy and climate change, pledging decarbonization strategies, and investing in alternative energies. Some even claim to be transforming into clean energy companies. Given a history of obstructive climate actions and “greenwashing”, there is a need to objectively evaluate current and historical decarbonization efforts and investment behavior. This study focuses on two American (Chevron, ExxonMobil) and two European majors (BP, Shell). Using data collected over 2009–2020, we comparatively examine the extent of decarbonization and clean energy transition activity from three perspectives: (1) keyword use in annual reports ( discourse ); (2) business strategies ( pledges and actions ); and (3) production, expenditures and earnings for fossil fuels along with investments in clean energy ( investments ). We found a strong increase in discourse related to “climate”, “low-carbon” and “transition”, especially by BP and Shell. Similarly, we observed increasing tendencies toward strategies related to decarbonization and clean energy. But these are dominated by pledges rather than concrete actions. Moreover, the financial analysis reveals a continuing business model dependence on fossil fuels along with insignificant and opaque spending on clean energy. We thus conclude that the transition to clean energy business models is not occurring, since the magnitude of investments and actions does not match discourse. Until actions and investment behavior are brought into alignment with discourse, accusations of greenwashing appear well-founded.

This paper examines populist claims-making in US presidential elections. We define populism as a discursive strategy that juxtaposes the virtuous populace with a corrupt elite and views the former as the sole legitimate source of political power. In contrast to past research, we argue that populism is best operationalized as an attribute of political claims rather than a stable ideological property of political actors. This analytical strategy allows us to systematically measure how the use of populism is affected by a variety of contextual factors. Our empirical case consists of 2,406 speeches given by American presidential candidates between 1952 and 1996, which we code using automated text analysis. Populism is shown to be a common feature of presidential politics among both Democrats and Republicans, but its prevalence varies with candidates' relative positions in the political field. In particular, we demonstrate that the probability of a candidate's reliance on populist claims is directly proportional to his distance from the center of power (in this case, the presidency). This suggests that populism is primarily a strategic tool of political challengers, and particularly those who have legitimate claims to outsider status. By examining temporal changes in populist claims-making on the political left and right, its variation across geographic regions and field positions, and the changing content of populist frames, our paper contributes to the debate on populism in modern democracies, while integrating field theory with the study of institutional politics.

This article explores and critically evaluates the potential contribution to discourse studies of topic modelling, a group of machine learning methods which have been used with the aim of automatically discovering thematic information in large collections of texts. We critically evaluate the utility of the thematic grouping of texts into ‘topics’ emerging from a large collection of online patient comments about the National Health Service (NHS) in England. We take two approaches to this, one inspired by methods adopted in existing topic modelling research and the other using more established methods of discourse analysis. In the study, we compare the insights produced by each approach and consider the extent to which the automatically generated topics might be of use to discourse analysts attempting to organise and study sizeable datasets. We found that the topic modelling approach was able to group texts into ‘topics’ that were truly thematically coherent with a mixed degree of success, while the more traditional approach to discourse analysis consistently provided a more nuanced perspective on the data which was ultimately closer to the ‘reality’ of the texts it contains. This study thus highlights issues concerning the use of topic modelling and offers recommendations and caveats to researchers employing such approaches to studying discourse in the future.

We still know little of why strategy processes often involve participation problems. In this paper, we argue that this crucial issue is linked to fundamental assumptions about the nature of strategy work. Hence, we need to examine how strategy processes are typically made sense of and what roles are assigned to specific organizational members. For this purpose, we adopt a critical discursive perspective that allows us to discover how specific conceptions of strategy work are reproduced and legitimized in organizational strategizing. Our empirical analysis is based on an extensive research project on strategy work in 12 organizations. As a result of our analysis, we identify three central discourses that seem to be systematically associated with nonparticipatory approaches to strategy work: \"mystification,\" \"disciplining,\" and \"technologization.\" However, we also distinguish three strategy discourses that promote participation: \"self-actualization,\" \"dialogization,\" and \"concretization.\" Our analysis shows that strategy as practice involves alternative and even competing discourses that have fundamentally different kinds of implications for participation in strategy work. We argue from a critical perspective that it is important to be aware of the inherent problems associated with dominant discourses as well as to actively advance the use of alternative ones.