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This study examines the web accessibility issues faced by users with disabilities when using ChatGPT, a popular chatbot. It is crucial for users with disabilities to have barrier-free access to Internet communications technology to be on par with other users. Because of its roots in artificial intelligence (AI) technology, ChatGPT can empower individuals with various abilities, providing access to the Internet and potentially leading to a substantial boost in digital inclusion for these users.

The researchers focused on ensuring ease of access to ChatGPT’s webpage to achieve the study objective. They conducted manual testing with a visually impaired researcher. They used axe DevTools and Accessibility Insights to investigate the target page’s three most commonly used states for accessibility issues.

The researchers identified substantial and crucial web accessibility issues on the target page. These issues resulted in frustration and hindered complete access to information about ChatGPT’s features. The researchers stress the significance of prioritising web accessibility and urge web designers to integrate Web Content Accessibility Guidelines (WCAG) standards into the initial stages of web development rather than addressing them as corrective measures. Given the United Nations' recognition of access to information and communication technology (ICT) as a pivotal Sustainable Development Goal (SDG) for users with disabilities, it is imperative to elevate web accessibility to foster their economic self-reliance and independence. This study underscores this imperative.

In this study, researchers assessed the accessibility of ChatGPT on the Google Chrome and Microsoft Edge browsers. This investigation could potentially be broadened to encompass additional web browsers. Furthermore, the researchers focused on three distinct states of ChatGPT: the initial default state, the subsequent output state and the third state, which represents errors on the target page. Further, developers can employ the results to enhance the accessibility experience for users with varying abilities who interact with ChatGPT.

Following a comprehensive examination of the current body of literature, the study pinpointed a gap in research, highlighting the necessity to conduct accessibility assessments for ChatGPT with regard to these particular users.

The widespread application of smart technologies in services not only brings efficiency and convenience to consumers but also inevitably comes with negative effects. Therefore, this article aims to illustrate the impact of privacy invasion on consumers' intention to use smart services. Using distrust as a mediating variable, compare two different modes of interaction between voice and text, and study the positive impact of privacy commitment. This study aims to provide recommendations for smart service providers to make the consumer experience better.

This paper adopts an experimental approach, with data collection and hypothesis analysis by designing four different experiments.

The results show that the negative impact of privacy invasion on consumers' intention to use smart services is moderated by privacy commitments and interaction modes. This article verifies the mediating effect of distrust on consumers' intention to use when privacy invasion occurs and verifies the moderating effect of the interaction modes by comparing voice interaction with text interaction and demonstrates that text interaction mode will attenuate the mediating role of distrust in the path in privacy invasion. Besides, it also indicates that privacy commitments can moderate the relationship between privacy invasion’s effect on distrust and intention to use.

Focusing on privacy invasion, this study explores consumers' intention to use smart services, compares the two interaction modes of voice and text to explore their moderating effects, deeply explores consumer psychology and studies the mediating role of distrust and the moderating role of privacy commitment.

The study aims to provide concrete service remediation and enhancement for LLM developers such as getting user forgiveness and breaking through perceived bottlenecks. It also aims to improve the efficiency of app users' usage decisions.

This paper takes the user reviews of the app stores in 21 countries and 10 languages as the research data, extracts the potential factors by LDA model, exploratively takes the misalignment between user ratings and textual emotions as user forgiveness and perceived bottleneck and uses the Word2vec-SVM model to analyze the sentiment. Finally, attributions are made based on empathy.

The results show that AI-based LLMs are more likely to cause bias in user ratings and textual content than regular APPs. Functional and economic remedies are effective in awakening empathy and forgiveness, while empathic remedies are effective in reducing perceived bottlenecks. Interestingly, empathetic users are “pickier”. Further social network analysis reveals that problem solving timeliness, software flexibility, model updating and special data (voice and image) analysis capabilities are beneficial in breaking perceived bottlenecks. Besides, heterogeneity analysis show that eastern users are more sensitive to the price factor and are more likely to generate forgiveness through economic remedy, and there is a dual interaction between basic attributes and extra boosts in the East and West.

The “gap” between negative (positive) user reviews and ratings, that is consumer forgiveness and perceived bottlenecks, is identified in unstructured text; the study finds that empathy helps to awaken user forgiveness and understanding, while it is limited to bottleneck breakthroughs; the dataset includes a wide range of countries and regions, findings are tested in a cross-language and cross-cultural perspective, which makes the study more robust, and the heterogeneity of users' cultural backgrounds is also analyzed.

The paper aims to examine the impacts and ethics of utilizing Artificial Intelligence (AI) in Indian policing. It explores both the positive and negative consequences of using AI, as well as the ethical considerations that have be taken into account.

This study is based on secondary sources of information, such as national and international reports, journal articles, and institutional websites that discuss the use of AI technology by the police in India.

AI has proven to be effective in policing, from preventing crime to identifying criminals, by detecting potential crimes in advance with fewer resources and in more areas. In India, the police use AI technology not only for facial recognition but also for crime mapping, analysis, and building blocks. However, factors such as caste, religion, language, and gender continue to cause conflict. India has shown a strong interest in using AI technology for policing, and wishes to accelerate its implementation in various policing contexts, including law and order. This paper calls for an assessment of the complexities and uncertainties brought about by new technologies in policing with ethical considerations.

This paper can provide valuable insights for policy-makers, academics, and practitioners engaged in discussions and debates concerning the ethical considerations associated with the adoption of AI tools in policing practices.

This study aims to investigate the performance analysis, science mapping and future direction of artificial intelligence (AI) technology, applications, tools and software used to preserve, curate and predict the historical value of cultural heritage.

This study uses the bibliometric research method and utilizes the Scopus database to gather data. The keywords used are “artificial intelligence” and “cultural heritage,” resulting in 718 data sets spanning from 2001 to 2023. The data is restricted to the years 2001−2023, is in English language and encompasses all types of documents, including conference papers, articles, book chapters, lecture notes, reviews and editorials.

The performance analysis of research on the use of AI to aid in the preservation of cultural heritage has been ongoing since 2001, and research in this area continues to grow. The countries contributing to this research include Italy, China, Greece, Spain and the UK, with Italy being the most prolific in terms of authored works. The research primarily falls under the disciplines of computer science, mathematics, engineering, social sciences and arts and humanities, respectively. Document types mainly consist of articles and proceedings. In the science mapping process, five clusters have been identified. These clusters are labeled according to the contributions of AI tools, software, apps and technology to cultural heritage preservation. The clusters include “conservation assessment,” “exhibition and visualization,” “software solutions,” “virtual exhibition” and “metadata and database.” The future direction of research lies in extended reality, which integrates virtual reality (VR), augmented reality (AR) and mixed reality (MR); virtual restoration and preservation; 3D printing; as well as the utilization of robotics, drones and the Internet of Things (IoT) for mapping, conserving and monitoring historical sites and cultural heritage sites.

The cultural heritage institution can use this result as a source to develop AI-based strategic planning for curating, preservation, preventing and presenting cultural heritages. Researchers and academicians will get insight and deeper understanding on the research trend and use the interdisciplinary of AI and cultural heritage for expanding collaboration.

This study will help to reveal the trend and evolution of AI and cultural heritage. The finding also will fill the knowledge gap on the research on AI and cultural heritage.

Some similar bibliometric studies have been conducted; however, there are still limited studies on contribution of AI to preserve cultural heritage in wider view. The value of this study is the cluster in which AI is used to preserve, curate, present and assess cultural heritages.

This paper aims to expand the scope and mitigate the biases of extant archival indexes.

The authors use automatic entity recognition on the archives of the Dutch East India Company to extract mentions of underrepresented people.

The authors release an annotated corpus and baselines for a shared task and show that the proposed goal is feasible.

Colonial archives are increasingly a focus of attention for historians and the public, broadening access to them is a pressing need for archives.

This paper aims to address the challenge raised in the literature regarding whether and how digitalization supports a servitized new product development (NPD) process, considering the customer’s involvement from the early stage of the process.

Pragmatic constructivism (PC) has been adopted for conceptualizing the NPD process as the construction of a new reality. PC is the method theory used for interpreting the field evidence drawn from a qualitative case study carried out at a multinational company operating in the semiconductor industry.

This study shows how digitalization supports the alignment to the overarching topoi of the company servitization strategy by enabling the integration and merging of different organizational topoi during the NPD process.

This study is confined to a single-case study and context.

The results of this study are relevant for managers involved in the stage-gate product development of manufacturing companies, informing them on how the use of digital tools enables or hinders the progression of product development projects.

This paper contributes to the servitization literature by offering field evidence that demonstrates the importance for manufacturing firms of acquiring customer feedback from an early NPD phase. Another contribution is related to the literature on the role of digitalization in NPD processes, describing how digital tools give support during the different phases of the NPD process.

In current era, retaining the best-performing employees has become essential for businesses to compete in the dynamic technological landscape. Consequently, organizations must ensure that their star performers believe that company’s reward and recognition (R&R) system is fair and equal. This study aims to use an explainable machine learning (eXML) model to develop a prediction algorithm for employee satisfaction with the fairness of R&R systems.

The current study uses state-of-the-art machine learning models such as Naive Bayes, Decision Tree C5.0, Random Forest and support vector machine-RBF to predict employee satisfaction towards fairness in R&R. The primary data used in the study has been collected from the employees of a large public sector undertaking from an emerging economy. This study also proposes a novel improved Naïve Bayes (INB) algorithm, the efficiency of which is compared with the state-of-the-art algorithms.

It is seen that the proposed INB model outperforms the state-of-the-art algorithms in many scenarios. Further, the proposed model and feature interaction are explained using the explainable machine learning (XML) concept. In addition, this study incorporates text mining techniques to corroborate the results from XML and suggests that “Transparency”, “Recognition”, “Unbiasedness”, “Appreciation” and “Timeliness in reward” are the most important features that impact employee satisfaction.

To the best of the authors’ knowledge, this is one of the first studies to use INB algorithm and mixed method research (text mining along with machine learning algorithms) for the prediction of employee satisfaction with respect to the R&R system.

The study aims to closely look at the phenomenon of transformational leadership and the psychological capital of followers by using affective process theory (APT). It has empirically tested the mediation of the perceived emotional labor (EL) of a leader and susceptible emotional contagion (EC) of followers when studying the effect of transformational leadership on the psychological capital (PsyCap) of followers.

The method adopted was mixed methodology. The data were collected from the 120 respondents and their perception regarding the construct as identified by previous literature was captured through a structured questionnaire. The relationships and hypotheses were tested by the structural equation modeling (SEM) model using SMART PLS. Further 20 semi-structured interviews were conducted using a qualitative approach.

The current research has empirically shown how specific aspects of transformational leadership, i.e. individual consideration perceived by followers also show high use of perceived deep acting strategy. Deep acting EL strategy is impacting positive EC and positive EC is leading to higher PsyCap of followers generating more work efficacy, hope, optimism and resilience. Mediation of positive EC between Deep acting EL and PsyCap was also observed. In qualitative studies done with the participants, major themes that emerged were transformational leadership, EL strategies, EC and PsyCap.

In times of uncertainty and stress after the post-COVID scenario, employees are facing emotional burnout due to increased work pressure and workload. Transformational leadership has become very critical to manage the PsyCap of followers by using correct EL strategies. Leaders can focus on the optimism and resilience aspect of PsyCap.

The current research has taken affective process theory (APT) as a foundation to understand the connection between transformational leadership and the PsyCap of followers. The study has specifically picked up the fourth mechanism of affective linkage as suggested by Elfenbein (2014) called emotional recognition and seen how emotions are transferred from source (leaders) to recipient (followers). The research has contributed by empirically testing the mediation of the perceived EL of leaders and the susceptible EC of followers and how they affect the PsyCap of followers.