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This is a general review study aiming to specify the key customer-based factors and technologies that influence the value co-creation (VCC) process through artificial intelligence (AI) and automation in the hospitality and tourism industry.

The study uses a theory-based general literature review approach to explore key customer-based factors and technologies influencing VCC in the tourism industry. By reviewing the relevant literature, the authors conclude a theoretical framework postulating the determinants of VCC in the AI-driven tourism industry.

This paper identifies customers' perceptions, attitudes, trust, social influence, hedonic motivations, anthropomorphism and prior experience as customer-based factors to VCC through the use of AI. Service robots, AI-enabled self-service kiosks, chatbots, metaversal tourism and new reality, machine learning (ML) and natural language processing (NLP) are technologies that influence VCC.

The results of this research inform a theoretical framework articulating the human and AI elements for future research set to expand the models predicting VCC in the tourism industry.

Few studies have examined consumer-related factors that influence their participation in the VCC process through automation and AI.

This paper aims to identify the combination of innovation determinants driving the creation of innovative products amongst market leaders and market followers in food and beverage (F&B) firms.

This research is based on the case study methodology by using two types of data sources: (1) semi-structured interviews with industry experts and (2) in-depth interviews with managers. In addition, a questionnaire adapted from prior research was used to consider market and firm types.

Suggesting an integrated theoretical framework based on firm-based factors and market-based factors, this study identified a combination of determinants significantly impacting innovative products in the market. Specifically, these determinants are competition intensity and innovation capability (a combination of research and development (R&D) investment and marketing capabilities). The study also examined how these determinants vary depending on whether the firms are market leaders or market followers.

This research provides practical insights for managers working in the F&B industry by using case studies and exploring the determinants of developing innovative products. In doing so, suitable strategies can be selected according to the market and firm situations.

The originality of the study is shown by focussing on how different combinations of market and firm factors could be applied in creating successful innovative products in the food sector.

Previous literature has identified human resources as a key source of competitive advantage in organizations of all sizes. However, Small and Medium-sized Enterprises (SMEs) face difficulty in comprehensively implementing all recommended Human Resource Management (HRM) functions. In this study, we shed light on the field of HRM in SMEs by focusing on the context of Greek Small and Medium-sized Hotels (SMHs), which represent a dominant private sector employer across the country.

Using a fuzzy-set qualitative comparative analysis (fsQCA) and 34 in-depth interviews with SMHs' owners/managers, we explore the HRM conditions leading to high levels of performance, while taking into consideration the influence of internal key determinants.

We uncover three alternative successful HRM strategies that maximize business performance, namely the Compensation-based performers, the HRM developers and the HRM investors. Each strategy fits discreet organizational characteristics related to company size, ownership type and organizational structure.

To the best of the authors' knowledge this is among the first empirical studies that examine different and equifinal performance-enhancing configurations of HRM practices in SMHs.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

This systematic review examined the emerging threat of indoor and outdoor pollutants to public health in Latin America and the Caribbean (LAC).

Pollutants and pollution levels are becoming an increasing cause for concern within the LAC region, primarily because of the rapid increase in urbanization and the use of fossil fuels. The rise in indoor and outdoor air pollutants impacts public health, and there are limited regional studies on the impact of these pollutants and how they affect public health. A comprehensive literature search was conducted using Google Scholar, PubMed, Scopus, EBSCOhost, Web of Science and ScienceDirect databases. Significant search terms included “indoor air pollution,” “outdoor air pollution,” “pollution,” “Latin America,” “Central America,” “South America” and “Caribbean was used.” The systematic review utilized the Rayyan systematic software for uploading and sorting study references.

Database searches produced 1,674 results, of which, after using the inclusion–exclusion criteria and assessing for bias, 16 studies were included and used for the systematic review. These studies covered both indoor and outdoor pollution. Various indoor and outdoor air pollutants linked to low birth weight, asthma, cancer and DNA impairment were reported in this review. Even though only some intervention programs are available within the region to mitigate the harmful effects of pollution, these programs need to be robust and appropriately implemented, causing possible threats to public health. Significant gaps in the research were identified, especially in the Caribbean.

Limitations of the study include limited available research done within LAC, with most of the research quantifying pollutants rather than addressing their impacts. Additionally, most studies focus on air pollution but neglect water and land pollution’s effects on public health. For this reason, the 16 studies included limited robustness of the review.

Although available studies quantifying pollution threats in LAC were identified in this review, research on the adverse impacts of pollution, especially concerning public health, is limited. LAC countries should explore making cities more energy-efficient, compact and green while improving the transportation sector by utilizing clean power generation. In order to properly lessen the effects of pollution on public health, more research needs to be done and implemented programs that are working need to be strengthened and expanded.

This study aims to examine the relationship between financial inclusion, CO₂ emissions and financial sustainability across 17 African countries.

Data were sourced from the World Development Indicators for the period 2004-2021. The study performs the principal component analysis, panel fixed effects model and quantile regression estimations to investigate the relationship between financial inclusion, CO₂ emissions and financial sustainability.

The study finds that an increase in automated teller machine (ATM) penetration rate, savings and credits increases CO₂ emissions. Findings also reveal that financial sustainability reduces financial inclusion, with significant negative effects on the conditional mean of CO₂ emissions and the conditional distribution of CO₂ emissions across quantiles.

This study is beneficial for policymakers, particularly in the age of digitalization and drive for low-carbon emissions, to develop green credits for energy players and investors to take up renewable and green energy projects characterized by high levels of carbon storage and carbon capture. Further, the banking sector’s credits and liquid assets should be used to finance alternative banking energy-related equipment and services, such as solar photovoltaic wireless ATMs, and fewer bank branches.