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This study aims to identify, review and synthesize existing literature on key theories, drivers and barriers affecting consumer adoption or resistance to artificial intelligence (AI) in the hospitality sector.

This study aims to conduct a complete literature review of the accrued knowledge generated so far on AI in the hospitality sector. To attain the overall objectives of this study, we used the systematic literature review (SLR) method. This method systematically handles the diversity of knowledge in a specific topic to answer precise research questions. It also generates new visions through a synthesis of the literature, to identify the knowledge gaps, set the new directions for the future researcher and provide sufficient guidance to inform the policy and practice.

The findings of this study are presented in three sections, as follows: descriptive analysis, content analysis and synthesized framework. The findings highlighted the state-of-the-art mapping of the existing research in terms of publication frequency over time and across publication outlets, key theories, methods and geographies. In addition, literature on consumer adoption (or resistance) of AI in hospitality is content analyzed to highlight key drivers and barriers. Moreover, this review critically evaluates extant literature and sets future agendas by postulating specific research questions for further knowledge development in this field of study.

The SLR focused on consumer adoption or resistance to use AI in hospitality literature. The future researcher may include additional streams to get better results.

The study findings will help multiple stakeholders to understand the underlying causes of customer resistance or barriers to the intention to use/adopt AI services in the hotel sector. Furthermore, study results will allow them to better analyze the relationship between customer barriers, intents or consumer decision behaviors.

First, this study provides a comprehensive synthesis of the literature on the consumer adoption or resistance of AI in hospitality. This study categorizes the existing diversified literature in two main themes – drivers and barriers – to present a simplistic picture of the existing literature. Second, the review highlights the gaps and limitations in existing research and provides guidance for future scholars. Third, the key contribution of this review is the development of a unified framework on the consumer adoption or resistance of AI in the hospitality sector. That is, this study puts forward the behavioral reasoning theory framework and suggests that future research using this lens will immensely contribute to existing literature. Finally, this study facilitates the practitioners to understand the key motivating and hindering factors affecting the adoption and resistance behavior.

The purpose of this study is to examine whether companies with high audit quality (AQ) are expected to use trade credit (TC) as a financing source. Traditionally, vendors are most likely to extend TC to creditworthy customers.

The author uses the data from 134,099 firm-year observations of nine Asian emerging markets from 2001 to 2017. Further, to check the impact of AQ on trade credit, the authors employ ordinary least square (OLS) with fixed effects, cluster effect regression and random effect.

The findings indicate that vendors extend more TC to the companies audited by the BIG4 auditors as, these independent practitioners have greater competencies, expert intellectual capital, global networking connections, and high investment in information technology. The authors, therefore, conjecture that the company's use of TC increases with their improved AQ, especially audited by BIG4. The results are found consistent with this prediction and robust to the alternative measures of trade credit. Similarly, this positive association is more pronounced with the BIG4 partner's unqualified audit opinion.

This study uses the sample of Asian Emerging countries but the researchers cannot generalize the results to developed countries or other regions.

This paper's findings have significant implications for the management, board of directors, shareholders and suppliers. Further, results are in favor of appointing BIG4 auditors to gain the trust of suppliers.

Despite the wide-ranging literature that discusses the importance of quality audits in enhancing the firms' financial disclosures that leads to better access to finance through investors and lenders. But the TC as a financing source is ignored in relation to AQ. The study’s results extend the literature associating companies' AQ with financial decisions.

The purpose of this study is to investigate the reasons (for and against) affecting owners' attitudes and intentions to use digital marketing (DM) strategies in tourism and hospitality (T&H) small and medium enterprises (SMEs), by employing the behavioral reasoning theory (BRT).

A survey method was used to collect data from 306 T&H (SMEs) owners and analyze the data through partial least square structural equation modelling (PLS-SEM).

The study results indicate that reasons significantly affect owners' attitudes and intentions to use DM in T&H (SMEs). The “RF” was as follows: perceived ease of use (PEU), perceived usefulness (PU), and trust in DM professionals” and the “RA” technological anxiety, usage barriers (UB), and perceived complexity (PC) effect on owners’ attitudes and intentions to use DM. Also, it indicates that openness to change value significantly affects the “RF” insignificant with “RA” and attitude.

The findings of this study emphasize that if owners/managers, governments, and policymakers overcome the “RA” that discourages and raises the knowledge about the “RF” in the adoption of DM, then the usage intention of DM strategies can rise in T&H (SMEs).

This study is the first to investigate the reasons (for and against) the owner’s attitude and intention to use DM in T&H (SMEs) by utilizing behavioral reasoning theory (BRT), technology acceptance model (TAM), and innovation resistance theory (IRT).

This article investigates the dynamic implication of healthcare expenditure on economic growth in the selected ten Sub-Saharan African countries over the period 2000–2018.

The study methodology included dynamic heterogenous panel, using mean group and pooled mean group estimators. The investigation of the healthcare expenditure and economic growth nexus was achieved while controlling the effects of investment, savings, labor force and life expectancy via interaction terms.

The results from linear healthcare expenditure have a significant positive impact on economic growth, while the nonlinear estimates through the interaction terms between healthcare expenditure and investment have a negative statistically significant impact on growth. The marginal effect of healthcare expenditure evaluated at the minimum and maximum level of investment is positive, suggesting the impact of health expenditure on growth does not vary with the level of investments. This result responds to the primary objective of the article.

In policy terms, the impact of investment on healthcare is essential to addressing future health crises. The impact of coronavirus disease 2019 (COVID-19) can never be separated from the shortages or low prioritization of health against other sectors of the economy. The article also provides an insight to policymakers on the demand for policy reform that will boost and make the health sector attractive to both domestic and foreign direct investment.

Given the vulnerability of SSA to the health crisis, there are limited studies to examine this phenomenon and first to address the needed investment priorities to the health sector infrastructure in SSA.

This bibliometric study aims to analyze publication trends, active countries, collaborations, influential citations and thematic evolution in learning analytics (LA) research focused on higher education (HE) during and after the COVID-19 lockdown period.

From the Scopus database, this bibliometric analysis extracts and evaluates 609 scholarly articles on LA in HE starting in 2019. The multidimensional process identifies the scope impacts, advancing the understanding of LA in HE. An analysis of co-citation data uncovers the key influences that have shaped the literature. This study uses the stimulus-organism-response (SOR) theory to suggest future research directions and organizational adaptations to new LA technologies and learner responses to LA-enabled personalized interventions.

Learning analytics are becoming important in the HE environment during and after the COVID-19 lockout. Institutions have used LA to collect socio-technical data from digital platforms, giving them important insights into learning processes and systems. The data gathered through LA has assisted in identifying areas for development, opening the path for improved student success and academic performance evaluation and helping students transition to the workforce.

The study’s concentration on the post-COVID-19 timeframe may lead to paying attention to potential pandemic developments. Nonetheless, the findings provide a thorough picture of LA’s contributions to HE and valuable ideas for future study initiatives. Future research with the SOR framework suggests areas for additional study to maximize LA’s potential in diverse HE situations.

This study adds to the growing corpus of knowledge on learning analytics in HE, especially in light of the COVID-19 lockdown and its aftermath. By using bibliometric analysis, the study provides a complete and evidence-based understanding of how LA has been used to address challenges related to HE. This study uses bibliometric analysis and SOR theory to appraise and map HE learning analytics research. The selected study themes can help scholars, educators and institutions shape their future efforts to improve teaching, learning and support mechanisms through learning analytics.