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Over the past decades, many health authorities and public policy experts have traditionally relied on indicators that are dependent on a nation's economy, its health-care infrastructure advancements, and superiority in biomedical sciences and technology to predict potential infection rates should a health pandemic occur. One such commonly relied-upon indicator was that of the Global Health Security (GHS) Index. However, the coronavirus disease 2019 (COVID-19) pandemic has shown how such variables prove to be inaccurate in predicting the infection rates during a global health pandemic. Hence, this paper proposes the utilization of socio-cultural behavioral traits to predict a country's COVID-19 infection rates.

This is achieved by proposing a model involving the classification and regression tree (CART) algorithm and a Poisson regression against the six selected cultural behavioral predictors consisting of individualism, power distance, masculinity, uncertainty avoidance, long-term orientation, and indulgence.

The results show that all the selected cultural behavioral predictors are significant in impacting COVID-19 infection rates. Furthermore, the model outperforms the conventional GHS Index model based on a means squared error comparison.

The authors hope that this study would continue promoting the use of cultures and behaviors in modeling the spread of health diseases.

The authors hope that their works could prove beneficial to public office holders, as well as health experts working in health facilities, in better predicting potential outcomes during a health pandemic, thus allowing them to plan and allocate resources efficiently.

The results are a testament to the fact that sociocultural behavioral traits are more reliant predictors in modeling cross-national infection rates of global health pandemics, like that of COVID-19, as compared to economic-centric indicators.

Mountaineering and related activities are increasingly becoming popular and are accompanied by an increase in medical incidents. Emergency operations in mountainous terrain are time-critical and often pose major logistical challenges for rescuers. Drones are expected to improve the operational performance of mountain rescuers. However, they are not yet widely used in mountain rescue missions. This paper examines the determinants that drive the behavioral intention of mountain rescuers to adopt drones in rescue missions.

This is a behavioral study that builds upon an extended model of the unified theory of acceptance and use of technology (UTAUT) and investigates the relationship between individual attitudes, perceptions, and intentions for drone adoption. Original survey data of 146 mountain rescuers were analyzed using moderated ordinary least squares (OLS) regression analysis.

Results indicate that the behavioral intention to use drones in mountain rescue missions is driven by the expected performance gains and facilitating conditions. Favorable supporting conditions and experience with drones further moderate the relationship between performance expectancy and behavioral intention. The effects for effort expectancy, social influence, and demonstrations were not significant.

Rescue organizations and stakeholders are recommended to consider the identified determinants in the implementation of drones in emergency logistics. Drone manufacturers targeting mountain rescue organizations are advised to focus on operational performance, provide sufficient support and training, and promote the gathering of practical experience.

A tailored-model that provides first empirical results on the relevance of personal and environmental factors for the acceptance of drones in emergency logistics is presented.

This study investigated how behavioral and emotional forms of engagement are associated with faculty support and student-faculty interactions among engineering students.

Quantitative research methods were used to analyze survey data from 781 undergraduates in seven large undergraduate engineering courses. Linear hierarchical regression models were used to evaluate the relationships between demographics (gender, race/ethnicity, family education, US status and transfer status) and student engagement and between faculty behaviors and engagement.

Faculty support was consistently, significantly and positively linked to all forms of student engagement, while student-faculty interactions were significantly and positively linked to effort and positive emotional engagement and negatively linked to attention and (an absence of) negative emotional engagement. Gender, race/ethnicity, international student status and transfer status significantly predicted at least one form of engagement.

Although this was a single institution study and cross-sectional, the findings suggest that faculty support and student-faculty interactions, while important for engagement, have different effects on different types of students. Faculty and teacher professional development efforts should raise awareness of these differences in order to enhance diversity and inclusion in engineering courses and curricula at all levels.

The analysis of behavioral and emotional forms of engagement represents more of a motivational lens on engagement in contrast to the traditional focus on time-on-task or time spent in fruitful educational practices, as is the norm with much of the engagement literature in higher education.

German (corporate-) real estate management departments have been facing the challenge of poor data quality for years. This holds them back from generating efficiency potentials via the use of new methods from the field of digitalisation and from coping with the increasing requirements from the ESG context. The purpose of this paper is to explore why German (corporate-) real estate managements (do not) share data with their real estate service providers to address the data quality challenge and identify possible solutions.

To answer the research question, the reasoned action approach, an established theory from psychology for predicting human behaviour, is used. The relationships between the constructs are determined using linear regression. The study participants are almost exclusively from Germany.

The organisational milieu (perceived behavioural control) has a significant impact on the behaviour of sharing data with real estate service providers. Especially the change of contractual arrangements (data-driven contracts) seems to be crucial for the improvement of information logistics.

To the best of the author’s knowledge, for the first time, the reasoned action approach is used within the German real estate industry to predict organisational behaviour in the context of digitalisation.

The primary purpose of this study is to integrate the stimulus-organism-behavior-accomplishment (S-O-B-A) paradigm to investigate the chain effect of university students' perceived university and family support on their entrepreneurial action (EA) with a serial mediation of their attitude and intention.

This study introduces stratified random sample to choose respondents and a cross-sectional research design. partial least square-structural equation modeling (PLS-SEM) has applied to thoroughly investigate the behavioral intention concerned with students' entrepreneurship action.

The findings explored that perceived university support and family supports positively impact students' entrepreneurship attitude, where perceived family support creates statistically more powerful implications than university support. Students' attitude toward entrepreneurship positively affects their entrepreneurial intent, and finally, the entrepreneurial pursuit has an affirmative impact on students' EA.

This study incorporates the S-O-B-A paradigm for the very first time to investigate the effects of students' environmental support on their EA with double mediation by their attitude and intention.

Technological advancements in games increased the popularity of online gaming. The rapid expansion of the eSports market may largely be attributed to the ever-increasing popularity amongst Generation Y amateur gamers. The primary objective of this study is to determine the factors influencing Generation Y amateur gamers' ongoing eSports gameplay intentions.

This study used the extended unified theory of acceptance as the theoretical framework. Data analysis included exploratory principal component analysis, confirmatory factor analysis and path analysis.

The results of the confirmatory factor analysis suggest that Generation Y amateur gamers' ongoing eSports gameplay intentions is an eight-factor model that is reliable, valid and has acceptable model fit. The results of the path analysis indicate that habit, price-value, flow, effort expectancy and facilitating conditions have a statistically significant positive influence on amateur gamers' ongoing eSports gameplay intentions, whilst social influence and hedonic motivation have a negative but non-significant influence on those intentions.

The sample was formed using only amateur eSports gamers. In this regard, the opportunity exists to research professional eSports gamers. This study only focussed on Generation Y members between 18 and 36 years old. As a result, there is an opportunity for researchers to research the different generations of South African eSports gamers to determine whether there are any differences or similarities between generational segments.

The results of this study clearly indicate that flow, together with habit are salient contributors to ongoing gameplay intentions amongst amateur eSports gamers in South Africa. A reasonable assumption that can be made here is that flow is also instrumental in encouraging habitual gaming, which increases the importance of flow in overall ongoing gameplay intentions. This suggests that R&D expenditure should be directed at enhancing user engagement by building increased levels of flow into eSports games.

eSports game developers can also achieve a desired state of flow by creating daily challenges that reward players when the players achieve specific objectives, which will encourage gamers to enter a state of flow when provided with challenges to complete. However, these in-game challenges should have a variety of levels regarding difficulty, ranging from beginner, intermediate and advanced levels so as not to exceed the effort expectancy of different groups of players. Game developers should provide regularly updated challenges to gamers to ensure that eSports games remain enjoyable and does not become predictable.

Given the nascence of research on eSports behaviour, the results of this study provide a novel addition to the knowledge pool, particularly in terms of amateur eSports behavioural intentions. Interestingly, hedonic motivation and social influence were non-significant negative predictors of Generation Y amateur gamers' ongoing eSports gameplay intentions. The recommendations provide various marketing strategies and opportunities for eSports business expansion.

Vendors’ social cues – physical or behavioural hints – have an impact on the professional buyer. However, little is known about that impact. The purpose of this paper is to place knowledge about the impact of social cues that other disciplines acquired in the context of business-to-business (B2B) marketing to contribute constructively to the research agenda.

By integrating findings on the processing of social cues and the behavioural response from the disciplines of neuroscience, biology and psychology (specifically the behavioural inhibition system [BIS]/behavioural activation system [BAS]-theory), this paper aims to provide an interdisciplinary perspective on the automatic evaluation of vendors by professional buyers.

Social cues are likely to be of substantial value in the (first) encounter between buyer and seller. Positively evaluated social cues create an approach-motivated behavioural intention, whereas negatively evaluated ones create avoidance. This process is probably predominantly mediated by trust and moderated by personality and contextual factors.

This paper stimulates research about the impact of social cues in a B2B context. While such knowledge would add practical value, this paper also explores possibilities for managers to use neuroscientific techniques to assess and train sales agents.

The impact of social cues is hardly covered in the B2B marketing literature, but they have an important impact on B2B decision-making. The conceptual framework combines the BIS/BAS theory (approach/avoidance) with the SOR-model (stimulus-organism-response), which is unique to the B2B marketing field.

This study aims to investigate the predictive technology acceptance models and their evolution in the tourism context. These predictive models make a knowledgeable decision about the possibility of future outcomes by analysing data. As futurists are interested in making a prediction about the likelihood of different behaviours over time, researchers of these predictive models have focussed on behaviour and predicting the intentions of users. This study proposes to demonstrate the revolution of these models and how are changed overtime. It also indicates the role of them in future studies.

By reviewing the predictive models and literature, this study looks in-depth in the process of alteration of these models.

This study explores the reasons of the evolution of predictive models and how they are changed. It shed light on the role of predictive models in future research and will suggest new directions for forthcoming studies.

One of the main limitations of this study is that as the world is currently struggling with COVID-19 and predictability of these models will be changed. As the future is disruptive, it cannot be concluded that how these models will be altered in future.

Role of predictive behavioural models of tourists is fundamentally crucial in assessing the performance of planners and marketers of tourism services in the future. It will also vastly helps the successful development of tourism sectors, and it has practical value for all tourism stakeholders.

Few studies have focussed on the evaluation of these models and their role in future research.

This study aimed to investigate the gendered nature of craft beer (CB) consumption in Italy and Germany.

Data were collected through online surveys in Italy (N = 210) and Germany (N = 211). Based on an enhanced version of the theory of planned behaviour, mean value difference tests and moderated regression analyses with gender as a moderator were performed to test gender effects on CB consumption behaviour.

The study results provide evidence that the gap in CB consumption behaviour is not very pronounced. In the German sample, gender did not moderate the effects of the model components on behavioural intent. However, the study found significant mean differences in all model variables. In the Italian sample, gender moderated the effects of several components of the theory of planned behaviour on behavioural intention. Hence, CB consumption appears to represent an opportunity for Italian women to negotiate their womanhood in a historically masculine-dominated space.

The limitations of these data are the focus on two specific countries, the use of small-sized samples and the prediction of behavioural intentions instead of actual behaviour.

The study may help marketing managers develop appropriate marketing strategies based on a better understanding of gender-specific needs in CB consumption.

This investigation provides the first comparative analysis of gender-specific behavioural patterns in CB consumption in two European countries characterised by notably different beer cultures.

Some aspects of technology behaviour remain unclear, such as the generation of technology use and engagement. So, this study aims to address the following question: does engagement with technology drive the use of technology?or does the use of technology create the engagement with technology?

Based on the uses and gratifications theory, this study compares three alternative competing models that explain technology behaviour on a sample of 715 individuals, using the selection criteria proposed by Mathieson. A comprehensive analysis and comparison of three structural competing models on technology behaviour, namely, “use-and-engagement”, “use-to-engagement” and “engagement-to-use”, are presented.

Findings show that the “use-and-engagement” model provides a better explanation of technology behaviour and is superior to predict technology behaviour, suggesting that both technology engagement and use could be considered as consequences.

This study’s major contribution is the empirical examination of three structural competing models and the selection of the best explaining model of technology behaviour.

Algunos aspectos del comportamiento tecnológico permanecen sin aclarar, como la creación del uso e implicación hacia la tecnología. Así que abordamos la siguiente pregunta: ¿La implicación con la tecnología impulse su uso?, o ¿es el uso de la tecnología el que impulse la implicación?.

Basándonos en la Teoría de los Usos y Gratificaciones se han comparado tres modelos alternativos que compiten entre sí para explicar el comportamiento tecnológico, en una muestra de 715 individuos utilizando el criterio de selección propuesto por Mathieson. Se presenta un análisis y una comparación exhaustive de tres modelos estructurales competitivos sobre el comportamiento tecnológico, que son “uso-e-implicación”, “uso-para-la implicación” e “implicación-para-el uso”.

Los resultados muestran que el modelo “uso-e-implicación” proporciona la mejor explicación del comportamiento tecnológico y es superior para predecir el comportamiento tecnológico, lo que sugiere que tanto la implicación como el uso de la tecnología podrían considerarse como consecuencias.

Nuestra principal contribución es el análisis empírico de tres modelos estructurales competitivos y la selección del mejor de ellos para explicar el comportamiento tecnológico.

技术行为的某些方面仍然不清楚, 例如技术使用和参与的产生。因此, 我们意在解决以下问题：对技术的参与是否推动了技术的使用,还是技术的使用创造了技术的参与？

基于 “使用与满足 “理论, 我们使用马蒂森提出的选择标准, 在715人的样本上比较了三种解释技术行为的替代竞争模型。即我们对三个关于技术行为的结构性竞争模型, “使用和参与”、“使用到参与 ”和 “参与到使用 ”进行了综合的分析和比较。

研究结果显示, “使用和参与 “模型更好的解释了技术行为, 并且其优于预测技术行为, 这表明技术参与和使用都可以被认为是后果。

我们的主要贡献是对三个结构性竞争模型进行了实证检验, 并选择了对技术行为的最佳解释模型。