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The use of Twitter by political parties and politicians has been well studied in developed countries. However, there is a lack of empirical work, which has examined the use of Twitter in developing countries. This study aims to explore the information-sharing patterns of Pakistani politicians through Twitter accounts during the pre-election campaign of 2018.

Data of three weeks of the official party accounts and the politicians running for prime minister were analysed. The mixed-methods approach has been used to analyse quantitative and qualitative data retrieved through Twitonomy.

It was found that the most active Twitter account belonged to the winning party. The prominent Twitter account functions were a call to vote, promotional Tweets, promises and Tweeting about party developments. The present study provides evidence that there is a difference between the Tweeting behaviour of established and emerging parties. The emerging party heavily posted about changing traditional norms/culture/practices.

The study contributed to existing knowledge and has practical implications for politicians, citizens and social media planners.

The present study was designed carefully and based on empirical research. The study is unique in its nature to fill the research and knowledge gap by adding a variety of Twitter functions used by politicians.

The purpose of this paper is to provide an overview of NodeXL in the context of news diffusion. Journalists often include a social media dimension in their stories but lack the tools to get digital photos of the virtual crowds about which they write. NodeXL is an easy to use tool for collecting, analysing, visualising and reporting on the patterns found in collections of connections in streams of social media. With a network map patterns emerge that highlight key people, groups, divisions and bridges, themes and related resources.

This study conducts a literature review of previous empirical work which has utilised NodeXL and highlights the potential of NodeXL to provide network insights of virtual crowds during emerging news events. It then develops a number of guidelines which can be utilised by news media teams to measure and map information diffusion during emerging news events.

One emergent software application known as NodeXL has allowed journalists to take “group photos” of the connections among a group of users on social media. It was found that a diverse range of disciplines utilise NodeXL in academic research. Furthermore, based on the features of NodeXL, a number of guidelines were developed which provide insight into how to measure and map emerging news events on Twitter.

With a set of social media network images a journalist can cover a set of social media content streams and quickly grasp “situational awareness” of the shape of the crowd. Since social media popular support is often cited but not documented, NodeXL social media network maps can help journalists quickly document the social landscape utilising an innovative approach.

This is the first empirical study to review literature on NodeXL, and to provide insight into the value of network visualisations and analytics for the news media domain. Moreover, it is the first empirical study to develop guidelines that will act as a valuable resource for newsrooms looking to acquire insight into emerging news events from the stream of social media posts. In the era of fake news and automated accounts, i.e., bots the ability to highlight opinion leaders and ascertain their allegiances will be of importance in today’s news climate.

The purpose of this research is to explore and define the digital maturity of events using the Industry 4.0 model (I4.0) to create a definition for Events 4.0 (E4.0) and to place various relevant technologies on a scale of digital maturity.

In a mixed methods approach, we carried out a qualitative social media analysis and a quantitative survey of tourism and events academics. These surveys and the thorough literature review that preceded them allowed us to map the digital technologies used in events to levels of a digital maturity model.

We found that engagement with technology at events and delegate knowledge satisfactorily coexists for and across a number of different experiential levels. However, relative to I4.0, event research and the events industry appear to be digitally immature. At the top of the digital maturity scale, E4.0 might be defined as an event that is digitally managed; frequently upgrades its digital technology; fully integrates its communication systems; and optimizes digital operations and communication for event delivery, marketing, and customer experience. We expect E4.0 to drive further engagement with digital technologies and develop further research.

This study has responded to calls from the academic literature to provide a greater understanding of the digital maturity of events and how events engage with digital technology. Furthermore, the research is the first to introduce the concept of E4.0 into the academic literature. This work also provides insights for events practitioners which include the better understanding of the digital maturity of events and the widespread use of digital technology in event delivery.

The use of social media has increased during the COVID-19 pandemic. Social media platforms provide opportunities to share news, ideas and personal stories. Twitter is used by citizens in Pakistan to respond and comment on emerging news stories and events. However, it is not known whether Twitter played a positive or negative role in spreading updates and preventive messages during the COVID-19 pandemic. The purpose of this study is to analyse content from Twitter during the pandemic.

NodeXL was used to retrieve data using the keyword وائرس کورونا (written in Urdu and which translates to Coronavirus). The first data set (Case Study 1) was based on 10,284 Twitter users from the end of March. The second data set (Case Study 2) was based on 10,644 Twitter users from the start of April. The theoretical lens of effective message framing was used to classify the most retweeted content on Twitter.

Twitter was used for personal and professional projections and included certain tweets included political motives even during the unfolding health crisis. There appeared to be very few successful attempts to use Twitter as a tool for health awareness and risk communication. The empirical findings indicate that the most retweeted messages were gain-framed and can be classified as personal, informative and political in nature.

The present study provides insights likely to be of interest to researchers, health organizations, citizens, government and politicians that are interested in making more effective use of social media for the purposes of health promotion. The authors also provide novel insights into the key topics of discussions, websites and hashtags used by Pakistani Twitter users during the COVID-19 pandemic.

The purpose of this study is to develop a bottom-up segmentation of people affected by neurodiversity using Twitter data.

This exploratory study uses content analysis of information shared by Twitter users over a three-month period.

Cultural currents affect how the label of “neurodiversity” is perceived by individuals, marketplace actors and society. The extent to which neurodiversity provides a positive or negative alternative to stigmatizing labels for mental disorders is shaped by differentiated experiences of neurodiversity. The authors identify five neurodiversity segments according to identifiable concerns and contextual dynamics that affect mental wellbeing. Analyzing Twitter data enables a bottom-up typology of stigmatized groups toward improving market salience.

To the authors knowledge, this study is the first to investigate neurodiversity using Twitter data to segment stigmatized consumers into prospective customers from the bottom-up.

Using data from Twitter, the purpose of this paper is to assess the coping behaviour and reactions of social media users in response to the initial days of the COVID-19-related lockdown in different parts of the world.

This study follows the quasi-inductive approach which allows the development of pre-categories from other theories before the sampling and coding processes begin, for use in those processes. Data was extracted using relevant keywords from Twitter, and a sample was drawn from the Twitter data set to ensure the data is more manageable from a qualitative research standpoint and that meaningful interpretations can be drawn from the data analysis results. The data analysis is discussed in two parts: extraction and classification of data from Twitter using automated sentiment analysis; and qualitative data analysis of a smaller Twitter data sample.

This study found that during the lockdown the majority of users on Twitter shared positive opinions towards the lockdown. The results also found that people are keeping themselves engaged and entertained. Governments around the world have also gained support from Twitter users. This is despite the hardships being faced by citizens. The authors also found a number of users expressing negative sentiments. The results also found that several users on Twitter were fence-sitters and their opinions and emotions could swing either way depending on how the pandemic progresses and what action is taken by governments around the world.

The authors add to the body of literature that has examined Twitter discussions around H1N1 using in-depth qualitative methods and conspiracy theories around COVID-19. In the long run, the government can help citizens develop routines that help the community adapt to a new dangerous environment – this has very effectively been shown in the context of wildfires in the context of disaster management. In the context of this research, the dominance of the positive themes within tweets is promising for policymakers and governments around the world. However, sentiments may wish to be monitored going forward as large-spikes in negative sentiment may highlight lockdown-fatigue.

The psychology of humans during a pandemic can have a profound impact on how COVID-19 shapes up, and this shall also include how people behave with other people and with the larger environment. Lockdowns are the opposite of what societies strive to achieve, i.e. socializing.

This study is based on original Twitter data collected during the initial days of the COVID-19-induced lockdown. The topic of “lockdowns” and the “COVID-19” pandemic have not been studied together thus far. This study is highly topical.

The purpose of this work is to study how different stakeholders of a football club engage with interactions online through Twitter. It analyses the football club’s Twitter network to discover influential actors and the topic of interest in their online communication.

The authors analysed the social networks derived from over two million tweets collected during football matches played by Manchester United. The authors applied social network analysis to discover influencers and sub-communities and performed content analysis on the most popular tweets of the prominent influencers.

Sub-communities can be formed around current affairs that are irrelevant to football, perhaps due to opportunistic attempts of using the large networks and massive attention during football matches to disseminate information. Furthermore, the popularity of tweets featuring different topics depends on the types of influencers involved.

The methods can help football clubs develop a deeper understanding of their online social communities. The findings can also inform football clubs on how to optimise their communication strategies by using various influencers.

Compared to previous research, the authors discovered a wide range of influencers and denser networks characterised by a smaller number of large clusters. Interestingly, this study also found that bots appeared to become influential within the network.

In the field of knitwear, dimensional stability is assumed as a critical problem that affects the quality and salability of a product. Although much work has been done in this area with a focus on the factors affecting fabric shrinkage, however, there is a lack of work on knitwears with respect to their dimensional stability. The purpose of this paper is to investigate the impact of stitching parameters and wash types on the dimensional properties of knitwear.

The crew-neck t-shirts were prepared by using pique knitted fabrics. Different sewing and finishing parameters were used that include stitch density, stitch type, stitching thread and wash type. The critical measurements of the selected garment are taken as output variables which are body width, sleeve length, body length and across shoulder. After laundering process, shrinkage percentage was calculated by using before-wash and after-wash measurements.

This study shows that the stitching parameters affect significantly on knitwear’s shrinkage. Thus, when patterns are being developed for the cutting of fabric, expected shrinkage, known as residual shrinkage, must be considered to avoid unexpected changes in garment shape.

This research will be useful for knitwear manufacturing industry.

This study aims to develop a moderated mediation model that enables the examination of the direct relationship between brand orientation (BO) and export performance, the mediating effects of external and internal branding capabilities on the BO-export performance link, and the moderating influence of institutional environment, i.e. regulatory turbulence and policy support.

A time-lag primary data was collected from two-wave survey of 684 cross-industry exporting small and medium-sized enterprises (SMEs) using an online-email based survey technique, and the research model was validated using ordinary least squares regression analysis in SPSSV.27 and Hayes’ PROCESS macroV.2.13.

Regression findings indicate that the relationship between BO and export performance is not direct, but rather mediated by means of both external and internal branding capabilities. It further helps to uncover the dual role of institutional environment, with regulatory turbulence weakening and policy support strengthening the indirect influences of BO on export performance via external and internal branding capabilities.

This study advances branding literature by conceptualizing and empirically testing the role of BO associated with internal and external branding capabilities and, subsequently, with export performance.

The research findings indicate that brand-oriented SMEs must actively engage in the development of branding capabilities to improve their export performance.

While brand creation is essential for the success and growth of SMEs competing in the worldwide marketplaces, there is a dearth of research explaining the underlying mechanisms and boundary conditions through which BO influences export performance.

This study aims to present a conceptual framework for understanding the dual orientations of market-based organizational learning (MBOL), namely, market orientation (MO) and learning orientation (LO), in the development of pricing capability (PC) with the goal of improving business performance (BP). This framework further explores the moderating effects of coordination mechanism (CM) and environmental dynamism (ED) on the PC–BP link and the mediating role of PC on the relationship between MBOL and BP.

This study applied the partial least squares structural equation modeling on survey data from 298 Indian small- and medium-sized enterprises (SMEs) operating in manufacturing and service sectors.

The findings indicate that MBOL significantly contributes to PC development, which in turn improves BP. Interestingly, PC acts as a partial mediator in the MO–BP link, as well as LO–BP link. Moreover, CM and ED strengthen the effect of PC on BP. Finally, MO and LO have substantial and distinct effects on PC and BP.

This study examines only one market-related capability, i.e. PC, considers multi-industry SMEs rather than specific large industries and uses cross-sectional instead of longitudinal data.

These findings are crucial from managerial standpoints because SMEs need to understand the MBOL dimensions, including MO and LO, and their significance in improving PC and BP.

Understanding how MBOL adoption contributes to superior performance is critical, but research in the SMEs context is still lacking. This study addresses a research gap by examining the impact of MBOL on BP, both directly and indirectly, through PC in the context of SMEs.