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The present study aimed to explore the consequences of perceived authenticity in artificial intelligence (AI) assistants and develop a serial-mediation architecture specifying causation of loyalty in human–AI relationships. It intended to assess the predictive power of the developed model based on a training-holdout sample procedure. It further attempted to map and examine the predictors of loyalty, strengthening such relationship.

Partial least squares structural equation modeling (PLS-SEM) based on bootstrapping technique was employed to examine the higher-order effects pertaining to human–AI relational intricacies. The sample size of the study comprised of 412 AI assistant users belonging to millennial generation. PLS-Predict algorithm was used to assess the predictive power of the model, while importance-performance analysis was executed to assess the effectiveness of the predictor variables on a two-dimensional map.

A positive relationship was found between “Perceived Authenticity” and “Loyalty,” which was serially mediated by “Perceived-Quality” and “Animacy” in human–AI relational context. The construct “Loyalty” remained a significant predictor of “Emotional-Attachment” and “Word-of-Mouth.” The model possessed high predictive power. Mapping analysis delivered contradictory result, indicating “authenticity” as the most significant predictor of “loyalty,” but the least effective on performance dimension.

The findings of the study may assist marketers to understand the relevance of AI authenticity and examine the critical behavioral consequences underlying customer retention and extension strategies.

The study is pioneer to introduce a hybrid AI authenticity model and establish its predictive power in explaining the transactional and communal view of human reciprocation in human–AI relationship. It exclusively provided relative assessment of the predictors of loyalty on a two-dimensional map.

This study aims to examine the impact of using ChatGPT on the Halal tourism experience. It examines the relationships among Halal-friendly travel motivations and satisfaction, revisit intention and electronic word-of-mouth (e-WoM) while testing the moderating effect of ChatGPT on the relationship between satisfaction and revisit intention.

This study employed a quantitative methodology. Using purposive sampling techniques, it approached about 800 tourists (from November 2023 to January 2024) from several halal tourism destinations in Indonesia. A total of 395 usable surveys were analyzed to test the relationships and moderation effects by SEM.

The study indicates that Halal-friendly travel motivations positively impact Muslim tourist satisfaction, which in turn influences e-WoM and revisit intention. Importantly, ChatGPT significantly moderates the relationship between satisfaction and revisit intention, thereby strengthening tourist loyalty for those using the AI tool.

The study’s findings provide practical guidelines for halal tourism providers to enhance Halal-compliant services and incorporate ChatGPT as an AI tool to boost Muslim travelers’ satisfaction, drive e-WoM and increase revisit intentions. AI technology gives Halal tourism companies an advantage in offering customized, immediate support, which leads to Muslim visitors becoming loyal.

The study fills a significant gap in the Halal tourism literature by examining AI’s impact on the market. It expands the Expectation-Confirmation Theory (ECT), the push-pull theory and word-of-mouth models in Halal tourism. It also contributes to AI adoption in Halal tourism by addressing how modern AI tools can influence tourist behaviors, improve satisfaction and encourage repeat visits.

This study examines the role of passion in gender-specific entrepreneurial responses in times of crises and how passion manifests itself in a digital environment. Entrepreneurial passion feeds energy, tenacity, self-confidence and momentum, creating added value for a given economy.

A qualitative approach was used to conduct semi-structured interviews. To reach the research objectives, the author examined a sample of 22 female entrepreneurs and 19 male entrepreneurs in a comparative design.

The results obtained through a qualitative study prove that entrepreneurial passion is a driver of self-confidence for entrepreneurs. Moreover, the nature of entrepreneurial passion differs across gender in times of crises. Indeed, female entrepreneurs try to take their lives into their own hands by creating their own destinies. They have seized opportunities arising from purely technological progress to create their own businesses on social networks and solve the unemployment problem. Meanwhile, male entrepreneurs pursue opportunities based on market supply and demand to increase their market share and face a crisis.

These findings offer novel insights into research on social media entrepreneurs. This study could help new entrepreneurs highlight their abilities, particularly those that are most salient and central.

This study adds a new dimension to the literature on the role and nature of entrepreneurial passion in times of crises.

This study aims to test the dynamic impact of public debt and economic growth on newly democratized African countries (South Africa and Namibia) and compare the findings with those of newly democratized European countries (Germany and Ukraine) during the period 1990–2022.

The methodology involves three stages: identifying the appropriate transition variable, assessing the linearity between public debt and economic growth and selecting the order m of the transition function. The linearity test helps identify the nature of relationships between public debt and economic growth. The wild cluster bootstrap-Lagrange Multiplier test is used to evaluate the model’s appropriateness. All these tests would be executed using the Lagrange Multiplier type of test.

The results signify the policy switch, as the authors find that the relationship between public debt and economic growth is characterized by two transitions that symbolize that the current stage of the relationship is beyond the U-shape; however, an S-shape. The results show that for newly democratized African countries, the threshold during the first waves was 50% of GDP, represented by a U-shape, which then transits to an inverted U-shape with a threshold of 65% of GDP. Then, for the European case, it was 60% of GDP, which is now 72% of GDP.

The findings suggest that an escalating level of public debt has a negative impact on economic growth; therefore, it is important to implement fiscal discipline, prioritize government spending and reduce reliance on debt financing. This can be achieved by focusing on revenue generation, implementing effective taxation policies, reducing wasteful expenditures and promoting investment and productivity-enhancing measures.

This paper aims to explore students’ intention to use and actual use of the artificial intelligence (AI)-based chatbot such as ChatGPT or Google Bird in the field of higher education in an emerging economic context like Bangladesh.

The present study uses convenience sampling techniques to collect data from the respondents. It applies partial least squares structural equation modeling (PLS-SEM) for analyzing a total of 413 responses to examine the study’s measurement and structural model.

The results explore that perceived ease of use (PEOU) negatively affects intention to adopt AI-powered chatbots (IA), whereas university students’ perceived usefulness (PU) influences their IA positively but insignificantly. Furthermore, time-saving feature (TSF), academic self-efficacy (ASE) and electronic word-of-mouth (EWOM) have a positive and direct impact on their IA. The finding also reveals that students' IA positively and significantly affects their actual use of AI-based chatbot (AU). Precisely, out of the five constructs, the TSF has the strongest impact on students’ intentions to use chatbots.

Students who are not aware of the chatbot usage benefits might ignore these AI-powered language models. On the other hand, developers of chatbots may not be conscious of the crucial drawbacks of their product as per the perceptions of their multiple users. However, the findings transmit a clear message about advantages to users and drawbacks to developers. Therefore, the results will enhance the chatbots’ functionality and usage.

The findings of the study alert the teachers, students and policymakers of higher educational institutions to understand the positive outcomes and to accept AI-powered chatbots such as OpenAI’s ChatGPT. Outcomes also notify the AI-product developers to boost the chatbot’s quality in terms of timeliness, user-friendliness, accuracy and trustworthiness.

This study has two main objectives. First, it aims to identify the opportunities and challenges influencing consumer purchase behaviors when ordering food through food aggregator platforms. Second, it seeks to determine consumer preferences for paid, owned and earned media (POEM) channels in the context of food aggregators.

Data were collected through semi-structured interviews with consumers who ordered food via aggregator platforms. Qualitative data analysis was conducted using NVivo 14.

The study identifies eight key themes representing the opportunities and challenges faced by food aggregators: (1) Streamlining and transparency in charges, (2) Enhancing speed, quality and reliability of delivery services, (3) Strategizing discounts and promotions to maximize customer engagement, (4) Ensuring food quality, packaging and accuracy in delivery, (5) Diversifying payment portfolios to cater to different customers, (6) Streamlining the ordering process and timely addressing of issues, (7) Normalizing pricing issues and (8) Ensuring the safety of delivery agents. The results indicate consumer preferences for POEM channels, showing a preference for earned media first, followed by owned media, with paid forms of communication ranking last.

The study enhances the understanding of consumer perceptions and the dynamics of media influence on purchasing decisions, contributing valuable insights to the existing body of knowledge.

The qualitative analysis highlights critical themes and areas that brands must address to maintain customer satisfaction. Identifying opportunities and challenges allows food aggregators to prioritize strategic initiatives effectively. Additionally, understanding customer preferences for POEM channels enables marketers to tailor their communication strategies to better align with consumer expectations.

The study highlights that while food aggregators provide convenience and flexibility for consumers, major concerns such as the quality and packaging of delivered food as well as the safety of delivery partners, also influence customer decisions when ordering through aggregators.

This research is novel in its approach, providing an in-depth qualitative analysis that captures the nuanced perspectives of consumers using food aggregators. By analyzing customer interactions and feedback, the study aims to offer actionable insights for enhancing service quality and meeting consumer expectations.

This study examined the impact of digital finance on SME financial inclusion in Africa.

The study obtained data from the International Monetary Fund's Financial Access Survey and World Development Indicators covering the period from 2011 to 2022. Heteroskedastic panels corrected standard errors (HPCSE) and feasible generalized least squares regressions were employed in the analysis.

The findings indicate that digital finance (volume and intensity) significantly improves SME financial inclusion in Africa.

Due to the paucity of data, the study covered only 17 African countries over 12 years (2011–2022).

The findings imply the need for African central banks and other relevant regulatory bodies to establish effective regulations mandating Deposit Money Banks and other financial institutions to operate agent banking. This would facilitate access to financial services for SME owners. Such measures could financially include more unbanked SME owners, especially those in rural areas. Moreover, these initiatives must be strongly supported by introducing user-friendly digital financial technologies and registering more financial technology (fintech) companies.

Implementing necessary measures to enhance access to digital financial services for SMEs in Africa is likely to reduce unemployment and poverty and contribute to the economic growth and development of the region.

This study provides empirical evidence showing how digital finance affects SME financial inclusion in Africa.

This study aims to analyse and understand customer sentiments and perceptions from neobanking mobile applications by using advanced machine learning and text mining techniques.

This study explores a substantial large data set of 330,399 user reviews available in the form of unstructured textual data from neobanking mobile applications. This study is aimed to extract meaningful patterns, topics, sentiments and themes from the data.

The results show that the success of neobanking mobile applications depends on user experience, security features, personalised services and technological innovation.

This study is limited to textual resources available in the public domain, and hence may not present the entire range of user experiences. Further studies should incorporate a wider range of data sources and investigate the impact of regional disparities on user preferences.

This study provides actionable ideas for neobanking service providers, enabling them to improve service quality and mobile application user experience by integrating customer input and the latest trends. These results can offer important inputs to the process of user interaction design, implementation of new features and customer support services.

This study uses text mining approaches to analyse neobanking mobile applications, which further contribute to the growing literature on digital banking and FinTech. This study offers a unique view of consumer behaviour and preferences in the realm of digital banking, which will add to the literature on the quality of service concerning mobile applications.

The present paper offers a literature review on the application of institutional theory in the context of small and medium-sized enterprises (SMEs). The study aims to synthesize existing research on how institutional factors, both formal and informal, shape the strategic behaviors of SMEs through the lens of new institutionalism and its three strands – rational-action neoinstitutionalism (RAN), social-constructionist neoinstitutionalism (SCN) and mediated-conflict neoinstitutionalism (MCN). It also identifies critical gaps and proposes avenues for future research.

Employing the B-SLR’s multi-method framework, this study integrates bibliometric analysis and a systematic literature review. The analysis examines 202 articles, categorizing them into four key research clusters related to the application of institutional theory to SMEs.

The literature on SMEs and institutional theory is framed around the following clusters: (1) institutional theory and innovation in SMEs, (2) institutional theory and the environmental sustainability of SMEs, (3) institutional theory and the internationalization of SMEs and (4) institutional theory and SME strategies in emerging markets.

This study represents the first literature review applying institutional theory to SMEs, offering a framework for understanding how institutional factors influence SME strategic decisions. The paper also identifies research gaps, proposing directions for future studies within each of the four clusters and contributes to bridging the fragmented research on SMEs and institutional theory.

This study aims to evaluate the safe haven property of several assets against the US and European stock markets during the Russo-Ukrainian War in a time–frequency framework.

This study uses the wavelet-based dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity (DCC-GARCH) methodology and wavelet coherence on daily returns for the S&P500, STOXX600 and 13 possible save haven assets.

The results show that wheat and corn are the best assets to use as hedges and safe havens for all types of investors. The second-ranked are energy commodities, which are hedges and safe havens for long-term investors. Gold, silver and palladium display hedging and safe haven qualities for medium- and long-term investment. However, cryptocurrencies, the Dow Jones sustainability index and Islamic indices do not act as safe havens for most holding periods.

These findings have significant implications for portfolio investment strategies in times of geopolitical risks.

The contributions of this study are twofold. First, several assets from different classes were analyzed as possible candidates for safe havens. Second, to the best of the authors’ knowledge, this is the first study to analyze safe haven property through different investment horizons for the US and the European stock market indices during the Russo-Ukrainian War.