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Purpose: This study aims to identify communication speech acts and transaction terms in online stores (Olshop) during live streaming on Facebook amid the COVID-19 pandemic and to understand communication patterns between sellers and buyers when shopping on Facebook live streaming in Indonesia.

The Need for Research: This research is motivated by the skill gap arising from increasing buying and selling transactions through live streaming on Facebook. Cultural and demographic shifts, along with the widespread availability of modern technologies and marketing 2.0 have resulted in the global population adopting social media at rates far beyond our use of the Internet, making a compelling case by example and analogy that social media has the potential to level the playing field and is effective in reaching their target market.

Methodology: The type of research is descriptive-qualitative using corpus data instruments. The data collection technique in this study was carried out by reading and observing the data and listening to speeches about buying and selling women’s equipment from various online stores on Facebook. Then select and sort the data designated as forms, strategies, and functions of speech acts in buying and selling transactions during live streaming on Facebook. The data analysis technique has three steps: (1) reducing the amount of data; (2) presenting the data; and (3) concluding.

Findings: The results show that there are four types of speech acts between sellers and buyers in the live-streaming online shop on Facebook, namely, assertive, directive, expressive, and commissive speech acts.

The purpose of the chapter was to examine the impact of customer care chatbots on customer satisfaction levels in the mobile telephony industry in Zimbabwe, with a special focus on the company Econet Wireless. This chapter shows the conceptual framework used. An online questionnaire was administered to a sample of 100 Econet Wireless subscribers who were selected using probability stratified random sampling from Zimbabwe’s 10 provinces. The research data were collected and analysed for correlation, and a multiple regression analysis was carried out to identify the relationship between customer satisfaction and the three customer service improvements brought in by the introduction of customer service chatbots. The study discovered that there is a positive relationship between customer satisfaction levels and each of the three customer service improvements brought in by customer service chatbots, namely customer service convenience, speed of response, and omnichannel strategies. This study thereby proves that the introduction of customer service chatbots in the mobile telephony industry in Zimbabwe can lead to an improvement in customer satisfaction levels. However, addressing service quality only as a determinant of customer satisfaction in isolation is not sufficient to fully improve customer satisfaction levels. Therefore, organisations that seek to improve their customer satisfaction should consider strategies that address all determinants of customer satisfaction, namely price, product quality, service quality, situational factors, and personal factors. This study contributes to the body of knowledge, particularly regarding the use of artificial intelligence (AI) for customer service in developing economies.

People with pre-existing mental health conditions are more likely to be affected by global crises. The Covid-19 pandemic has presented them with unique challenges, including reduced contact with the psychiatric rehabilitation and support systems. Thus, understanding the emotional experience of this population may assist mental health organizations in future global crises.

In this paper, researchers analyzed the discourse of the mentally ill during the Covid-19 pandemic, as reflected in Israeli Facebook groups: three private groups and one public group. Researchers explored the language, reactions, emotions and sentiments used in these groups during the year before the pandemic, outbreak periods and remission periods, as well as the period before the vaccine’s introduction and after its appearance.

Analyzing groups’ discourse using the collective emotion theory suggests that the group that expressed the most significant difficulty was the Depression group, while individuals who suffer from social phobia/anxiety and PTSD were less affected during the lockdowns and restrictions forced by the outbreak.

Findings may serve as a tool for service providers during crises to monitor patients’ conditions, and assist individuals who need support and help.

With 43.2 million coronavirus cases and 525,000 deaths in 2022, India ranked second worldwide, after the United States (84.6 million cases and 1 million deaths), according to the latest available June 2022 COVID-19 impact data.

Amid people’s growing mistrust in the government, India’s news media enhanced the nation’s distinguished designation as the world’s largest and most populous democracy. India’s news media inform, educate, empower, and entertain a surging population of 1.4 billion people, which is roughly one-sixth of the world’s people.

Drawing upon the media agendamelding theoretical framework, we conducted a case study research into interplay between two prominent democratic institutions, the media and the government, to analyze the role of the COVID-19 pandemic in redefining India’s networked society.

India’s COVID-19 pandemic aggravated internecine tensions between media and government relating to four key freedom issues: (1) world’s largest COVID-19 lockdown affecting 1.3 billion Indians from March 25, 2020 to August 2020 with extensions and five-phased re-openings, to restrict the spread of COVID-19; (2) Internet shutdowns; (3) media censorship during the 1975–1977 “Emergency”; and (4) unabated murders of journalists in India.

Although the COVID-19 pandemic caused deleterious problems debilitating the tensions between the media and the government, India’s journalists thrived by speaking truth to power. This study delineates key aspects of India’s media agendamelding that explicates how the people of India form their media agendas. India’s news audiences meld media messages from newspapers, television, and social media to form a picture of the issues, insights, and ideas that define their lives and times in the 21st century digital age.

There is limited research examining the relationship between social media and green food purchasing behaviour. In the current study, we examine the factors that affect consumers’ green food purchasing behaviour in Türkiye.

Data collected from the participants by survey method were analysed using Smart PLS 4.0 with IBM SPSS 26 and PLS to run SEM.

Our findings have revealed that from all the structural elements in the theory of planned behaviour (TPB), only attitude and perceived behavioural control contribute to consumers’ green food purchasing behaviour, while subjective norms do not contribute to consumers’ green food purchasing behaviour. Additionally, it was found that social media usage (SMU) and digital marketing interactions (DMI) have a positive and significant effect on green food purchasing behaviour. More information, experiences, opinions and recommendations on green foods in social media channels can encourage consumers to buy more green food.

This study first evaluates the applicability of the TPB model in explaining green food purchasing behaviour. This study is extended with two new factors included in the original framework of the TPB model, namely, SMU and DMI.

This study aims to investigate how a sample of US municipalities use social media for strategic communication, focusing on efforts to effectively measure and evaluate that communication. Research questions focus on measurement and evaluation practices, as well as the motivations and impacts associated with these practices.

This research uses a qualitative approach. Interviews were conducted with 12 municipal government personnel responsible for social media communication. The self-selected participants represent 10 states and all US Census regions. Data was content analyzed and categorized according to four research questions, with emergent themes described.

The results show a diverse set of approaches and motivations, with surface-level measurement and evaluation methods. Initial efforts at more ad hoc use of social media are moving toward more deliberate strategies, but limited resources inhibit progress for some municipalities.

Few studies exist which explore how US municipalities formally measure and evaluate their social media activities as part of their overall strategic communication efforts. This study adds to the existing literature by providing insight into the measurement and evaluation practices that municipalities use to assess their social media communication. The study also provides a basis for larger and deeper investigations of municipal strategic communication practices related to measurement and evaluation.

The booming social media attracts construction professionals (CPs) to express emotions caused by work pressure (WP) through online behaviors. Previous works focus on the analysis of WP and emotions but do not adequately consider how WP can be reflected through online emotions. Thus, this study aims to attempt to explore the quantitative relationship between online emotional intensity and WP.

This study developed a linguistic-sticker (LS) model to quantitatively evaluate the sentiment intensity of posts published on social media. Moreover, the authors designed two econometric models of ordinary least squares regression and negative binomial regression to test the hypothesis.

The research found that posts with stronger negative sentiment (or positive sentiment) indicate that CPs face higher (or lower) WP. Besides, there is a negative bias between the sentiment intensity of posts and the comment quantity.

The positive correlation between sentiment intensity of posts and WP has been confirmed, which indicates that construction managers should pay more attention to CPs' behavior on social media, and take a more direct way to analyze work-related online behavior (e.g. posting, commenting). The dynamic monitoring of emotion-related posts also provides a direct basis for the management team to learn about CP's pressure status and propose measures to reduce their negative emotions. Furthermore, the emotional posts published by CPs on social media provide a direct basis for team managers to obtain their psychological state.

The research contributes to incorporating CPs' emotions into the LS model and to providing information systems artifacts and new findings on the analysis of WP and online emotions.

This research sought to examine the differential effect of chatbot banking artificial intelligence (AI) on consumer experience in the banking industry. A positivist paradigm was adopted to sample 389 consumers who were previously exposed to chatbot banking in Zimbabwe. A causal research design was employed whilst a quantitative approach was followed. In analysing data, the research study applied the structural equation modelling (SEM) technique. The authors found that chatbot banking significantly improves customer experience (CX) in the banking industry. Reliability and responsiveness of the chatbot need to be enhanced for effective improvements in CX. A need was also identified to enhance CX through the development of an ease-to-use chatbot which is embedded in everyday messaging applications of consumers. A significant association was also found between perceived benefits of chatbot banking and CX. This study informs the development of competitive advantage by banks and other related companies through AI-based CX management strategies. In times of pandemics and beyond, chatbot banking can be very instrumental in improving CX.

The integration of artificial intelligence (AI) technologies like conversational AI and HR chatbots in international human resource development (HRD) presents both productivity benefits and ethical challenges. This study aims to examine the ethical dimensions of AI-driven HR chatbots, emphasizing the need for fairness, autonomy and nondiscrimination. It discusses inherent biases in AI systems and addresses linguistic, cultural and accessibility issues. The paper advocates for a comprehensive risk assessment approach to guide ethical integration, proposing a “risk management by design” framework. By embracing ethical principles and robust risk management strategies, organizations can navigate AI-driven HR technologies while upholding fairness and equity in global workforce management.

Systematic literature review.

The paper advocates for a comprehensive risk assessment approach to guide ethical integration, proposing a “risk management by design” framework.

By embracing ethical principles and robust risk management strategies, organizations can navigate AI-driven HR technologies while upholding fairness and equity in global workforce management.

This study explores the intricate ethical landscape surrounding AI-driven HR chatbots, spotlighting the imperatives of fairness, autonomy, and nondiscrimination. Uncovering biases inherent in AI systems, it addresses linguistic, cultural, and accessibility concerns. Proposing a pioneering “risk management by design” framework, the study advocates for a holistic approach to ethical integration, ensuring organizations navigate the complexities of AI-driven HR technologies while prioritizing fairness and equity in global workforce management.

Image Processing is an emerging field that is used to extract information from images. In recent years, this field has received immense attention from researchers, especially in the research domains of object detection, Biomedical Imaging and Semantic segmentation. In this study, a bibliometric analysis of publications related to image processing in the Science Expanded Index Extended (SCI-Expanded) has been performed. Several parameters have been analyzed such as annual scientific production, citations per article, most cited documents, top 20 articles, most relevant authors, authors evaluation using y-index, top and most relevant sources (journals) and hot topics.

The Bibliographic data has been extracted from the Web of Science which is well known and the world's top database of bibliographic citations of multidisciplinary areas that covers the various journals of computer science, engineering, medical and social sciences.

The research work in image processing is meager in the past decade, however, from 2014 to 2019, it increases dramatically. Recently, the IEEE Access journal is the most relevant source with an average of 115 publications per year. The USA is most productive and its publications are highly cited while China comes in second place. Image Segmentation, Feature Extraction and Medical Image Processing are hot topics in recent years. The National Natural Science Foundation of China provides 8% of all funds for Image Processing. As Image Processing is now becoming one of the most critical fields, the research productivity has enhanced during the past five years and more work is done while the era of 2005–2013 was the area with the least amount of work in this area.

This research is novel in this regard that no previous research focuses on Bibliometric Analysis in the Image Processing domain, which is one of the hot research areas in computer science and engineering.