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This paper aims to uncover patterns of Mexican craft beer consumers and guide companies’ decisions in the creation of new products, marketing strategies, advertising and promotion to increase craft beer sales and contribute to faster growth.

This is a conjoint analysis with a selection of attributes for new or renewed products, marginal disposition to pay for particular characteristics through brand-specific choice-based design, and market simulation.

This paper clearly demonstrates consumers’ preferences and willingness to pay in Mexico, with a cutting-edge market research technique combining the prioritization of preferred craft beer characteristics, and the price consumers are willing to pay for such product characteristics.

The study's sample size of 501 responses is relatively small compared to the total number of craft beer consumers in Mexico. To enhance the validity and reliability of the findings, future studies should aim to obtain larger samples and compare their results with those of this study.

This study has important implications for craft beer producers, allowing them to develop targeted craft beers with appealing attributes for Mexican consumers, such as color, aroma intensity, alcohol degree intensity, bitterness, foam level and price.

This study's market forecasting simulation technique is based on assumptions of consumer behavior and market dynamics. Although relevant variables were considered, unanticipated external factors or market changes could impact the forecasts' accuracy. This will allow for a more comprehensive understanding of craft beer consumer preferences in different markets and enhance the reliability of forecasting techniques.

This paper informs craft beer producers by providing valuable knowledge on customers’ preferences and willingness to pay to enhance craft beer companies’ product development processes.

COVID-19 has affected the economies adversely from all sides. The sudden halt in production has impacted both the supply and demand sides. It calls for analysis to quantify the impact of the reduction in economic activity on the economy-wide variables so that appropriate steps can be taken. This study aims to evaluate the sensitivity of various sectors of the Indian economy to this dual shock.

The eight-sector open economy general equilibrium Global Trade Analysis Project (GTAP) model has been simulated to evaluate the sector-specific effects of a fall in economic activity due to COVID-19. This model uses an economy-wide accounting framework to quantify the impact of a shock on the given equilibrium economy and report the post-simulation new equilibrium values.

The empirical results state that welfare for the Indian economy falls to the tune of 7.70% due to output shock. Because of demand–supply linkages, it also impacts the inter- and intra-industry flows, demand for factors of production and imports. There is a momentous fall in the demand for factor endowments from all sectors. Among those, the trade-hotel-transport and manufacturing sectors are in the first two positions from the top. The study recommends an immediate revival of the manufacturing and trade-hotel-transport sectors to get the Indian economy back on track.

The present study has modified the existing GTAP model accounting framework through unemployment and output closures to account for the impact of change in sectoral output due to COVID-19 on the level of employment and other macroeconomic variables.

This study aims to identify significant factors, analyse them using the consistent fuzzy preference relations (CFPR) method and forecast the probability of successful deployment of the internet of things (IoT) and cloud computing (CC) in Gujarat, India’s building sector.

From the previous studies, 25 significant factors were identified, and a questionnaire survey with personal interviews obtained 120 responses from building experts in Gujarat, India. The questionnaire survey data’s validity, reliability and descriptive statistics were also assessed. Building experts’ opinions are inputted into the CFPR method, and priority weights and ratings for probable outcomes are obtained to forecast success and failure.

The findings demonstrate that the most important factors are affordable system and ease of use and battery life and size of sensors, whereas less important ones include poor collaboration between IoT and cloud developer community and building sector and suitable location. The forecasting values demonstrate that the factor suitable location has a high probability of success; however, factors such as loss of jobs and data governance have a high probability of failure. Based on the forecasted values, the probability of success (0.6420) is almost twice that of failure (0.3580). It shows that deploying IoT and CC in the building sector of Gujarat, India, is very much feasible.

Previous studies analysed IoT and CC factors using different multi-criteria decision-making (MCDM) methods to merely prioritise ranking in the building sector, but forecasting success/failure makes this study unique. This research is generally applicable, and its findings may be utilised for decision-making and deployment of IoT and CC in the building sector anywhere globally.

The purpose of this paper is to explore the support for multi-party collaboration in project construction provided by building information modeling (BIM). Based on the perspective of value co-creation, the research results can provide support for the collaborative application and contract design of BIM platform.

In this paper, an evolutionary game model involving the owner, designer and constructor is constructed by using prospect theory and evolutionary game theory. Through simulation analysis, the evolution law of the strategy choice of each party in the collaborative application of BIM platform is discussed and the key factors affecting the strategy choice of all parties are analyzed.

The results show that there is an ideal local equilibrium point with progressive stability in the evolutionary game between the three parties: “the construction party shares information, the designer receives the information and optimizes the project and the owner does not provide incentives”; in addition, the opportunistic behaviors of the design and construction parties, as well as the probability of such behaviors being detected and the subsequent punishment have a significant impact on the evolutionary outcome.

This method can provide support for the collaborative application and contract design of BIM platform.

This study aims to explore the extent to which Chinese multinational enterprises (MNEs) rely on supranational institution – the Belt and Road Initiative (BRI) – versus host country institutional quality to navigate their foreign location choice.

This study uses a conditional logit regression model using a sample of 1,302 greenfield investments by Chinese MNEs in 54 BRI participating countries during the period 2011–2018.

The results indicate that as a supranational institution, the BRI serves as a substitution mechanism to address the deficiencies in institutional quality in BRI participating countries, thereby attracting Chinese MNEs to invest in those countries. In addition, the BRI’s substitution effect on host country institutional quality is more pronounced for large MNEs, MNEs in the manufacturing industry and MNEs in inland regions.

This study expands the understanding of the BRI as a supranational institution for MNEs from emerging markets and reveals its substitution effect on the host country institutional quality. Furthermore, it highlights that MNEs with diverse characteristics gain varying degrees of benefits from the BRI.

The construction industry is widely recognized as one of the most hazardous sectors in the world. Despite extensive research on safety management, a critical issue remains that insufficient attention is devoted to safety practices in rural areas. Notably, accidents frequently occur during the construction of rural self-built houses (RSH) in China. Safety management tends to be overlooked due to the perceived simplicity of the construction process. Furthermore, it is essential to acknowledge that China currently lacks comprehensive laws and regulations governing safety management in RSH construction. This paper aims to analyze the behavior of key stakeholders (including households, workmen, rural village committee and the government) and propose recommendations to mitigate safety risks associated with RSH construction.

This paper applies evolutionary game theory to analyze the symbiotic evolution among households, workmen and rural village committee, in situations with or without government participation. Additionally, numerical simulation is utilized to examine the outcomes of various strategies implemented by the government.

Without government participation, households, workmen, and rural village committee tend to prioritize maximizing apparent benefits, often overlooking the potential safety risks. Numerical simulations reveal that while government involvement can guide these parties towards safer decisions, achieving the desired outcomes necessitates the adoption of reasonable and effective strategies. Thus, the government needs to offer targeted subsidies to these stakeholders.

Considering that during the construction phase, stakeholders are the main administrators accountable for safety management. However, there exists insufficient research examining the impact of stakeholder behavior on RSH construction safety. This study aims to analyze the behavior of stakeholders about how to reduce the safety risks in building RSH. Thus, the authors intend to contribute to knowledge in this area by establishing evolutionary game model. Firstly, this study carried out a theoretical by using tripartite evolutionary game to reveal the reasons for the high safety risk during building RSH. Practically, this research points out the important role of households, workmen and rural village committee in improving safety management in rural areas. Besides, some suggestions are proposed to the government about how to reduce construction safety risks in rural areas.

This study aims to explore the connection between demographic traits and the choice of attraction patterns among international repeat tourists.

The study employed a questionnaire survey to collect data from 1550 international repeat tourists who visited Tanzania between November 2022 and July 2023. Convenient sampling was employed as tourists were selected from the three international airports of Tanzania, namely Kilimanjaro International Airport, Julius Nyerere International Airport, and Abeid Aman Karume International Airport. A multinomial logistic regression model was used to examine the impact of socio-demographic characteristics on the selection of attraction patterns among international repeat tourists.

The study revealed that demographic factors, including age, marital status, income level, occupation, and education level, exhibit statistically significant correlations with preferences for distinct attraction patterns. This significance was established through a p-value of less than 0.05 for all the aforementioned variables.

This study is primarily focused on international repeat tourists, thereby limiting insights into the preferences of domestic tourists. To better inform strategies aimed at attracting a larger domestic tourist base, future research may prioritize the investigation of choice of attractions patterns among domestic tourists in relation to their demographic characteristics.

This study contributes to the nuanced understanding of international tourist behavior by unraveling the extent to which demographic traits impact tourists’ choices of attraction patterns, thereby providing insights crucial for effective marketing strategies, improved visitor experiences, and sustainable tourism development strategies.

The research objective starts from the argument that small-scale multinational corporations’ (SMNCs’) managerial behavior toward auditing decisions is influenced by their personal value, especially when the auditing process is not mandatory. This study aims to examine how national culture-religiosity affects that decision. The authors further examine how foreign-owned MNCs might behave differently from local MNCs, although the host country’s cultural-religiosity value might influence that decision.

This study obtains the data from three sources: Hofstede Framework, Pew Research Center and World Bank Enterprise Survey in cross-sectional mode. The final sample consists of 8,590 SMNCs from 45 countries as the observations. This study uses robust regression analysis to test the effects of culture, religiosity and controlling shareholders on the audited financial statements decision.

The regression results support the hypothesis, whereas cultural-religiosity values are associated with the audited financial report. The findings confirm stakeholder theory and institutional theory.

This study fills a gap in the literature by providing empirical evidence on the cultural and religiosity effects on the accounting decision of SMNCs. The results can be used as the foundation for future research related to MNCs’ managerial behavior toward accounting policies, especially with the psychosocial factors.

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 study aims to examine the influence of ownership structure and board composition on the probability and intensity of stock repurchases. The study’s sample comprises 3,744 firm-year observations, consisting of 53 repurchasing firms with 96 firm-year observations from 2008 to 2019.

Probit and fixed-effects regression models are used to obtain empirical results. Moreover, a probit model with a continuous endogenous regressor (IV-probit) and an instrumental variable method with two-stage least squares (IV-2SLS) estimation are used to address endogeneity.

Corporations with high family or state ownership tend to inhibit stock repurchases to hoard excess free cash flow, supporting agency theory. Conversely, firms with high board independence tend to repurchase their stocks at least once to distribute free cash flows to shareholders, confirming agency theory. Nonetheless, corporations with more female directors on the board or CEO duality tend to conduct stock repurchases at least once but do not repurchase stocks with high values. Interestingly, more female directors on the board may send false signals about undervalued stocks.

This is the first study to reveal that firms with CEO duality repurchase their stocks at least once but avoid repurchasing shares with high values. It is also the first study to explore whether women on a board may cause false signaling about undervalued stocks. Furthermore, this study reveals that family and state ownership are potential determinants of stock repurchases in countries with high ownership concentration. This is the first study to address this issue in Thailand.