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This study aims to investigate the relevance, accuracy, specificity and justification of investment recommendations of generative artificial intelligence (GenAI) chatbots for different investment capitals and countries (UK and Bulgaria).

A two-stage mixed methods approach was used. Prompts were queried into OpenAI’s ChatGPT, Microsoft Bing and Google Bard (now Gemini). Finance and investment practitioners and finance and investment lecturers assessed the chatbots’ recommendations through an online questionnaire using a five-point Likert scale. The Chi-squared test, Wilcoxon-signed ranks test, Mann–Whitney U test and Friedman test were used for data analysis to compare GenAIs’ recommendations for the UK and Bulgaria across different amounts of investment capital and to assess the consistency of the chatbots.

GenAI chatbots’ responses were found to perform medium-to-high in terms of relevance, accuracy, specificity and justification. For the UK sample, the amount of investment had a marginal effect but prompt timing had an interesting impact. Unlike the British sample, the GenAI application, prompt timing and investment amount did not significantly influence the Bulgarian respondents’ evaluations. While the mean responses of the British sample were slightly higher, these differences were not statistically significant, indicating that ChatGPT, Bing and Bard performed similarly in both the UK and Bulgaria.

The study assesses the relevance, accuracy, specificity and justification of GenAI chatbots’ investment recommendations for two different periods, investment amounts and countries.

The purpose of this paper is to find underlying causes of leadership failure in NPM-based reforms in the public service, and propose leadership principles to guard against such failure as leaders meet the demands of a changing, complex public service environment.

An analysis of the managerial philosophy of Dr Berwick and its effects on CMS overall performance and employee morale.

NPM-based reforms create an environment in which administrative wrongdoing can occur. The principles of leadership found in NPLT, which advocates values-based, relationship-based distributed leadership provide a template for effective leadership that can reverse and possibly prevent leadership failure due to NPM-based reforms.

The authors used a single case to demonstrate NPLT leadership principles can reverse leadership failure in a Federal Agency. No quantitative analysis is attempted in this paper. The authors choice of papers to use in the literature review was subjective.

This paper identifies NPM-based reforms as a partial explanation for leadership failure in the public service, and also identifies the leadership principles needed to address and prevent such failures. It provides support for the use of NPLT as a template for effective public service leadership.

ONLY as events recede can we view them in proper perspective. It is then that we discover how often initial judgments were wrong, our fears con‐founded or our hopes dispelled. Treaties to end wars, pacts of eternal friendship and alliance are the debris which litter our uneasy world.

As organizations increasingly engage with consumers over mobile devices, there is a growing need to understand how consumers react to impression management over platforms with limited textual content. The purpose of this paper is to empirically assess how different impression management tactics can be used in mobile media to enhance consumer perception-attitude-intentions toward a corporate brand.

We surveyed 670 consumers and estimate structural equation models and repeated-measures ANOVAs to determine how short passages employing alternate impression management tactics influence consumers’ perceptions, attitudes and purchase intentions.

Results reveal that each impressions management tactic (i.e. ingratiation, intimidation, organizational promotion, supplication and exemplification) influences consumer perceptions, attitudes and intentions. The authors compare differences in how the impressions management tactics influence each stage of the perception-attitude-intentions model and find evidence that initial differences in perceptions favoring ingratiation and exemplification appeals become magnified for purchase intentions.

Recent calls for research focus on an understanding of how consumers process information on reduced-content platforms of small-screened mobile devices. These results provide empirical evidence of the use of impression management and the difference between five impression management tactics on enhancing consumer perception-attitude-intentions model.

The results of this study will provide marketers with insights to optimize communications and corporate brands with consumers over mobile media.

This paper adds to the nascent yet vital literature on mobile marketing by focusing on how impression management tactics influence perceptions, attitudes and intentions through the short message characteristic of mobile platforms. The authors develop a framework for how corporate brand management can strategically use impressions management tactics in this novel domain.

This research explores two interconnected questions: (1) How do we approach stylistic features of multimodal rhetorical artifacts such as protest posters? (2) Do said artifacts designed for different purposes exhibit systematic stylistic differences? Drawing on Charles Sanders Peirce’s semiotic categorization, this study develops a framework for examining concision, one of the primary stylistic considerations for multimodal rhetorical artifacts such as protest posters. This paper illustrates the use of this framework by exploring the correlation between rhetorical purpose and concision in posters created and disseminated before and during the 2011–2012 Québécois student movement. This study fine-tunes our existing knowledge on multimodality with style sensitivity, and demonstrates how an economy-of-sign based semiotic approach could enrich the empirical examination of multimodal rhetorical artifacts by generating more controlled interpretations.

The purpose of this paper is to analyze the readability and level of word complexity of search engine results pages (SERPs) snippets and associated web pages between Google and Bing.

The authors employed the Readability Test Tool to analyze the readability and word complexity of 3,000 SERPs snippets and 3,000 associated pages in Google and Bing retrieved on 150 search queries issued by middle school children.

A significant difference was found in the readability of SERPs snippets and associated web pages between Google and Bing. A significant difference was also observed in the number of complex words in snippets between the two engines but not in associated web pages. At the engine level, the readability of Google and Bing snippets was significantly higher than associated web pages. The readability of Google SERPs snippets was at a much higher level than those of Bing. The readability of snippets in both engines mismatched with the reading comprehension of children in grades 6–8.

The data corpus may be small. Analysis relied on quantitative measures.

Practitioners and other mediators should mitigate the readability issue in SERPs snippets. Researchers should consider text readability and word complexity simultaneously with other factors to obtain the nuanced understanding of young users’ web information behaviors. Additional theoretical and methodological implications are discussed.

This study measured the readability and the level of word complexity embedded in SERPs snippets and compared them to respective web pages in Google and Bing. Findings provide further evidence of the readability issue of SERPs snippets and the need to solve this issue through system design improvements.

The purpose of this paper is to investigate image search and retrieval problems in selected search engines in relation to Persian writing style challenges.

This study is an applied one, and to answer the questions the authors used an evaluative research method. The aim of the research is to explore the morphological and semantic problems of Persian language in connection with image search and retrieval among the three major and widespread search engines: Google, Yahoo and Bing. In order to collect the data, a checklist designed by the researcher was used and then the data were analyzed by descriptive and inferential statistics.

The results indicate that Google, Yahoo and Bing search engines do not pay enough attention to morphological and semantic features of Persian language in image search and retrieval. This research reveals that six groups of Persian language features include derived words, derived/compound words, Persian and Arabic Plural words, use of dotted T and the use of spoken language and polysemy, which are the major problems in this area. In addition, the results suggest that Google is the best search engine of all in terms of compatibility with Persian language features.

This study investigated some new aspects of the above-mentioned subject through combining morphological and semantic aspects of Persian language with image search and retrieval. Therefore, this study is an interdisciplinary research, the results of which would help both to offer some solutions and to carry out similar research on this subject area. This study will also fill a gap in research studies conducted so far in this area in Farsi language, especially in image search and retrieval. Moreover, findings of this study can help to bridge the gap between the user’s questions and search engines (systems) retrievals. In addition, the methodology of this paper provides a framework for further research on image search and retrieval in databases and search engines.

This chapter presents a novel exploration of generative Artificial Intelligence (AI) applications within the context of digital transformation in higher education, specifically focusing on the social sciences. Despite the interest in AI technologies for educational enhancement, there remains a notable gap in the literature: a comprehensive comparison of generative AI tools and their effectiveness in social science education. Addressing this gap, our study serves a dual purpose. Initially, we delineate the potential roles and benefits of generative AI in the pedagogical processes of teaching and learning. Subsequently, we conduct an empirical analysis of five leading generative AI engines to assess their performance and applicability in educational settings. Through this investigation, we aim not only to equip educators with a deeper understanding of the advantages and limitations of current AI technologies but also to inform AI developers about the specific needs of social science educators. The goal is to foster the development of more refined and effective generative AI solutions that can significantly enhance educational outcomes in the digital era.

The purpose of this paper is to determine the effect of the chosen search engine results page (SERP) on the website-specific hit count estimation indicator.

A sample of 100 Spanish rare disease association websites is analysed, obtaining the website-specific hit count estimation for the first and last SERPs in two search engines (Google and Bing) at two different periods in time (2016 and 2017).

It has been empirically demonstrated that there are differences between the number of hits returned on the first and last SERP in both Google and Bing. These differences are significant when they exceed a threshold value on the first SERP.

Future studies considering other samples, more SERPs and generating different queries other than website page count (<site>) would be desirable to draw more general conclusions on the nature of quantitative data provided by general search engines.

Selecting a wrong SERP to calculate some metrics (in this case, website-specific hit count estimation) might provide misleading results, comparisons and performance rankings. The empirical data suggest that the first SERP captures the differences between websites better because it has a greater discriminating power and is more appropriate for webometric longitudinal studies.

The findings allow improving future quantitative webometric analyses based on website-specific hit count estimation metrics in general search engines.

The website-specific hit count estimation variability between SERPs has been empirically analysed, considering two different search engines (Google and Bing), a set of 100 websites focussed on a similar market (Spanish rare diseases associations), and two annual samples, making this study the most exhaustive on this issue to date.