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Abstract

Details

Toxic Humans
Type: Book
ISBN: 978-1-83753-977-2

Article
Publication date: 2 March 2023

Cheryl Klimaszewski

Personal museums provide the conceptual catalyst for liking as a research approach and inclusivity around “idiosyncratic” knowledges within information research. An adapted…

Abstract

Purpose

Personal museums provide the conceptual catalyst for liking as a research approach and inclusivity around “idiosyncratic” knowledges within information research. An adapted research paper format echoes the approach of personal museums: as a commentary on the limits of institutional shaping for the field.

Design/methodology/approach

Personal museums are conceptualized as spaces of knowing in-formation, ontological openings that are literally and figuratively entered into, that make a difference to human and material ways of knowing. Karen Barad's agential realism and Sianne Ngai's vernacular aesthetic categories provide the theoretical lenses through which the researcher's 2018 visit to one personal museum is revisited.

Findings

An ethnographic account of the author's visit to the Communist Consumer Museum (CCM) in Timişoara, Romania shows how its improvisational, friendly and intimate atmosphere exposes it as a space of entanglements in a quantum sense, emphasizing the inseparability of human and material realms and how knowledges are always in-formation. Such entanglements create atmospheres generative of different ways of thinking about information and knowledge.

Originality/value

Human expressions of liking reveal material agencies as ways of knowing and information beyond the realm of human experience and meaning. A vernacular aesthetics of liking is presented as a way to resist the marginalizing tendencies of knowledges classified as unconventional, idiosyncratic or eccentric. This approach is one way of resisting the assumptions of channel thinking that often shape how information is studied.

Article
Publication date: 22 January 2024

Lingshu Hu

This study develops a computational method to investigate the predominant language styles in political discussions on Twitter and their connections with users' online…

Abstract

Purpose

This study develops a computational method to investigate the predominant language styles in political discussions on Twitter and their connections with users' online characteristics.

Design/methodology/approach

This study gathers a large Twitter dataset comprising political discussions across various topics from general users. It utilizes an unsupervised machine learning algorithm with pre-defined language features to detect language styles in political discussions on Twitter. Furthermore, it employs a multinomial model to explore the relationships between language styles and users' online characteristics.

Findings

Through the analysis of over 700,000 political tweets, this study identifies six language styles: mobilizing, self-expressive, argumentative, narrative, analytic and informational. Furthermore, by investigating the covariation between language styles and users' online characteristics, such as social connections, expressive desires and gender, this study reveals a preference for an informational style and an aversion to an argumentative style in political discussions. It also uncovers gender differences in language styles, with women being more likely to belong to the mobilizing group but less likely to belong to the analytic and informational groups.

Practical implications

This study provides insights into the psychological mechanisms and social statuses of users who adopt particular language styles. It assists political communicators in understanding their audience and tailoring their language to suit specific contexts and communication objectives.

Social implications

This study reveals gender differences in language styles, suggesting that women may have a heightened desire for social support in political discussions. It highlights that traditional gender disparities in politics might persist in online public spaces.

Originality/value

This study develops a computational methodology by combining cluster analysis with pre-defined linguistic features to categorize language styles. This approach integrates statistical algorithms with communication and linguistic theories, providing researchers with an unsupervised method for analyzing textual data. It focuses on detecting language styles rather than topics or themes in the text, complementing widely used text classification methods such as topic modeling. Additionally, this study explores the associations between language styles and the online characteristics of social media users in a political context.

Details

Online Information Review, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1468-4527

Keywords

Book part
Publication date: 5 October 2023

Susanne Sandberg, Igor Laine, Gesine Haseloff, Andreea I. Bujac and John E. Reilly

This chapter proposes authentic leadership as a generic competence and an integral part of doctoral education regardless of field of study. The authors explore its potential to…

Abstract

This chapter proposes authentic leadership as a generic competence and an integral part of doctoral education regardless of field of study. The authors explore its potential to enhance the development of doctoral candidates and academics and search for answers to the questions: Can and should authentic leadership be developed as a generic competence in doctoral education? How can it be designed and implemented in a doctoral training module? What would its learning outcomes be? The authors address these questions in the context of doctoral education. They assert that authentic leadership training should be mandatory for all doctoral candidates, and that supervisors should be actively engaged in the development of this underappreciated transferrable skill.

Open Access
Article
Publication date: 19 May 2023

Lars-Erik Gadde and Håkan Håkansson

In today’s business settings, most firms strive to closely integrate their resources and activities with those of their business partners. However, these linkages tend to create…

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Abstract

Purpose

In today’s business settings, most firms strive to closely integrate their resources and activities with those of their business partners. However, these linkages tend to create lock-in effects when changes are needed. In such situations, firms need to generate new space for action. The purpose of this paper is twofold: analysis of potential action spaces for restructuring; and examination of how action spaces can be exploited and the consequences accompanying this implementation.

Design/methodology/approach

Network dynamics originate from changes in the network interdependencies. This paper is focused on the role of the three dual connections – actors–activities, actors–resources and activities–resources, identified as network vectors. In the framing of the study, these network vectors are combined with managerial action expressed in terms of networking and network outcome. This framework is then used for the analysis of major restructuring of the car industries in the USA and Europe at the end of the 1900s.

Findings

This study shows that the restructuring of the car industry can be explained by modifications in the three network vectors. Managerial action through changes of the vector features generated new action space contributing to the transition of the automotive network. The key to successful exploitation of action space was interaction – with individual business partners, in triadic constellations, as well as on the network level.

Originality/value

This paper presents a new view of network dynamics by relying on the three network vectors. These concepts were developed in the early 1990s. This far, however, they have been used only to a limited extent.

Details

Journal of Business & Industrial Marketing, vol. 38 no. 13
Type: Research Article
ISSN: 0885-8624

Keywords

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