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This research examines the factors that affect business owner influencers' success in growing their businesses and making them a brand in line with the principles of the cognitive response theory (CRT).

This research examined the perceived uniqueness and originality of the posts on Nusr-et’s Instagram account as external information; information credibility was examined as a cognitive response, and desire was taken as a cognitive response and intention. Partial least squares structural equation modeling (PLS-SEM) was used in the analysis of the data.

According to the research findings, perceived uniqueness and originality were found to have positively affected information credibility and cognitive response, while cognitive response factors were also found to have positively affected desire. In addition, cognitive response factors were found to have mediated the effect of external information factors on desire. All these results reveal the significant accuracy of the model developed based on the CRT. In addition, age and gender variables were found to have had moderating roles. Based on the research findings, original suggestions for restaurant enterprises have been presented to help them gain a competitive advantage.

This study has found that the posts shared by business owner influencers have affected their followers in their desire to eat in the promoted restaurants and therefore, entrepreneurs and owners of the food and beverage industry should give importance to the preparation of social media content that could directly affect customers for visit to their restaurants and regularly post such contents in their social media accounts.

This research has been one of the first research papers using a model to reveal the reasons for behavioral intention in the field of hospitality based on the CRT.

Spaciousness is defined as “the feeling of openness or room to wander” that has been affected by various physical factors. The purpose of this paper is to assess the spaciousness of space to determine how spacious the space is. Furthermore, the study intends to propose a fuzzy-based model to assess the degree of spaciousness in terms of physical parameters such as area, proportion, the ratio of window area to floor area and color value.

Fuzzy logic is the most appropriate mathematical model to assess uncertainty using nonhomogeneous variables. In contrast to conventional methods, fuzzy logic depends on partial truth theory. MATLAB Fuzzy Logic Toolbox was used as a computational model including a fuzzy inference system (FIS) using linguistic variables called membership functions to define parameters. As a result, fuzzy logic was used in this study to assess the spaciousness degree of design studios in universities in the Iraqi Kurdistan region.

The findings of the presented fuzzy model show the degree to which the input variables affect a space perceived as larger and more spacious. The relationship between parameters has been represented in three-dimensional surface diagrams. The positive relationship of spaciousness with the area, window-to-floor area ratio and color value has been determined. In contrast, the negative relationship between spaciousness and space proportion is described. Moreover, the three-dimensional surface diagram illustrates how the changes in the input values affect the spaciousness degree. Besides, the improvement in the spaciousness degree of the design studio increases the quality learning environment.

This study attempted to assess the degree of spaciousness in design studios. There has been no attempt carried out to combine educational space learning environments and computational methods. This study focused on the assessment of spaciousness using the MATLAB Fuzzy Logic toolbox that has not been integrated so far.

The compressive strength of concrete depends on many interdependent parameters; its exact prediction is not that simple because of complex processes involved in strength development. This study aims to predict the compressive strength of normal concrete and high-performance concrete using four datasets.

In this paper, five established individual Machine Learning (ML) regression models have been compared: Decision Regression Tree, Random Forest Regression, Lasso Regression, Ridge Regression and Multiple-Linear regression. Four datasets were studied, two of which are previous research datasets, and two datasets are from the sophisticated lab using five established individual ML regression models.

The five statistical indicators like coefficient of determination (R²), mean absolute error, root mean squared error, Nash–Sutcliffe efficiency and mean absolute percentage error have been used to compare the performance of the models. The models are further compared using statistical indicators with previous studies. Lastly, to understand the variable effect of the predictor, the sensitivity and parametric analysis were carried out to find the performance of the variable.

The findings of this paper will allow readers to understand the factors involved in identifying the machine learning models and concrete datasets. In so doing, we hope that this research advances the toolset needed to predict compressive strength.

Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully investigated. This paper aims to investigate the effects traffic data set have on the performance of machine learning (ML) predictive models in AQ prediction.

To achieve this, the authors have set up an experiment with the control data set having only the AQ data set and meteorological (Met) data set, while the experimental data set is made up of the AQ data set, Met data set and traffic data set. Several ML models (such as extra trees regressor, eXtreme gradient boosting regressor, random forest regressor, K-neighbors regressor and two others) were trained, tested and compared on these individual combinations of data sets to predict the volume of PM_2.5, PM₁₀, NO₂ and O₃ in the atmosphere at various times of the day.

The result obtained showed that various ML algorithms react differently to the traffic data set despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%.

This research is limited in terms of the study area, and the result cannot be generalized outside of the UK as some of the inherent conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research, therefore, leaving out a few other ML algorithms.

This study reinforces the belief that the traffic data set has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form of traffic data set in the development of an AQ prediction model. This implies that developers and researchers in AQ prediction need to identify the ML algorithms that behave in their best interest before implementation.

The result of this study will enable researchers to focus more on algorithms of benefit when using traffic data sets in AQ prediction.

The purpose of this paper is to examine the current state of the literature on the variables associated with self-harm and aggression in women who committed a criminal offence.

Studies were identified through online databases, namely, PsycINFO, PubMed, ERIC and EBSCOhost, as well as manual searches of reference lists of the selected studies. The target population included women who committed a criminal offence and have engaged in self-harm and aggressive behaviors during their incarceration, either in correctional institutions or in forensic psychiatric settings.

Of the 1,178 studies identified, nine met inclusion criteria. The studies were conducted in six different countries and included data from 6360 female participants. Few studies examine self-harm and aggression in women who committed a criminal offence which speaks to the still sparse literature on this topic. This review of the association between self-harm and aggression in women offenders highlights the finding that a small group of women is often involved in both self-harm and aggression. The authors have identified possible psychological factors associated with women engaging in both self-harm and aggression. The findings also reveal a possible connection between types of aggressive behaviors and specific time periods during sentences or stays in forensic psychiatry.

The findings of this scoping review have clinical implications which may be considered by both researchers and the case management teams of women involved in both self-harm and aggression.

Despite the limited number of studies examining self-harm and aggression in women, this scoping review highlights gaps in the literature as well as notable psychological correlates of women who engage in self-harm and aggression.

Culture is considered a critical aspect of social media usage. The purpose of this paper is to explore how cultures and languages influence multilingual users' cross-cultural information sharing patterns.

This study used a crowdsourcing survey with Amazon Mechanical Turk to collect qualitative and quantitative data from 355 multilingual users who utilize two or more languages daily. A mixed-method approach combined statistical, and cluster analysis with thematic analysis was employed to analyze information sharing patterns among multilingual users in the Chinese cultural context.

It was found that most multilingual users surveyed preferred to share in their first and second language mainly because that is what others around them speak or use. Multilingual users have more diverse sharing characteristics and are more actively engaged in social media. The results also provide insights into what incentives make multilingual users engage in social media to share information related to Chinese culture with the MOA model. Finally, the ten motivation factors include learning, entertainment, empathy, personal gain, social engagement, altruism, self-expression, information, trust and sharing culture. One opportunity factor is identified, which is convenience. Three ability factors are recognized consist of self-efficacy, habit and personality.

The findings are conducive to promoting the active participation of multilingual users in online communities, increasing global resource sharing and information flow and promoting the consumption of digital cultural content.

Despite the vast potential of mobile phone use, grape smallholder farmers’ satisfaction with mobile phone use has attracted insufficient attention among scholars in Tanzania. The study examined factors influencing satisfaction with mobile phone use for accessing agricultural marketing information.

The study used a cross-sectional research design and a mixed research method. Structured questionnaire and focus group discussions were used to collect primary data from 400 sampled grape smallholder farmers. Data were analysed inferentially involving two-way analysis of variance, ordinal logistic regression and thematic analysis.

The findings indicate a statistically significant disparity in grape smallholder farmers’ satisfaction across different types of agricultural marketing information. Grape smallholder farmers exhibited higher satisfaction levels concerning information on selling time compared to all other types of agricultural marketing information (price, buyers, quality and quantity). Factors influencing grape smallholder farmers’ satisfaction with mobile phone use were related to perceived usefulness, ease of use, experience and cost.

This study contributes to scientific knowledge by providing actionable insights for formulating unique strategies for smallholder farmers’ satisfaction with agricultural marketing information.

The research aims to develop an assessment framework that evaluates critical success factors (CSFs) for the Quality 4.0 (Q 4.0) transition among Indian firms.

The authors use the fuzzy-Delphi method to validate the results of a systematic literature review (SLR) that explores critical aspects. Further, the fuzzy decision-making trial and laboratory (DEMATEL) method determines the cause-and-effect link. The findings indicate that developing a Q 4.0 framework is essential for the long-term success of manufacturing companies. Utilizing the power of digital technology, data analytics and automation, manufacturing companies can benefit from the Q 4.0 framework. Product quality, operational effectiveness and overall business performance may all be enhanced by implementing the Q 4.0 transition framework.

The study highlights significant awareness of Q 4.0 in the Indian manufacturing sector that is acquired through various means such as training, experience, learning and research. However, most manufacturing industries in India still follow older quality paradigms. On the other hand, Indian manufacturing industries seem well-equipped to adopt Q 4.0, given practitioners' firm grasp of its concepts and anticipated benefits, including improved customer satisfaction, product refinement, continuous process enhancement, waste reduction and informed decision-making. Adoption hurdles involve challenges including reliable electricity access, high-speed Internet, infrastructure, a skilled workforce and financial support. The study also introduces a transition framework facilitating the shift from conventional methods to Q 4.0, aligned with the principles of the Fourth Industrial Revolution (IR).

This research exclusively examines the manufacturing sector, neglecting other fields such as medical, service, mining and construction. Additionally, there needs to be more emphasis on the Q 4.0 implementation frameworks within the scope of the study.

This may be the inaugural framework for transitioning to Q 4.0 in India's manufacturing sectors and, conceivably, other developing nations.

The digital transformation revolution has brought outstanding changes to business organizations, especially in the digital accounting transformation domain. Consequently, the purpose of this study is to explore the important role of digital accounting transformation in improving business performance in the context of the banking industry.

Data were collected through a questionnaire from the Jordanian bank sector with a sample of 190 respondents. Partial least squares structural equation modeling (PLS-SEM) was used to analyze the collected data and test the hypotheses.

The results have shown that the adoption of digital accounting, adoption of FinTech innovation and technological competition are the major drivers for improving business performance. All direct paths leading to improving business performance were found to be significant in the hypothesized directions, while technological savvy was found to indirectly affect the relationship between (the adoption of digital accounting and FinTech innovation) and improving business performance.

The current study is differentiated from other studies by developing a theoretical research model to incorporate the adoption of digital accounting, adoption of FinTech innovation, technological competition, technological savvy and business performance in the Jordanian context under the digital transformation revolution. For practitioners, the findings provide policymakers with meaningful insight for organizations looking to adopt these digital technologies for improved business performance.

This study aims to investigate the effect of servant leadership on employee organizational identification and career satisfaction through the mediating lens of leader-member-exchange (LMX). Furthermore, this study also examines whether perceived organizational support (POS) strengthens the positive effect of servant leadership on LMX and subsequently, on employee organizational identification and career satisfaction.

Survey data were collected from 314 respondents working in hotels in United States of America (USA). Structural equation modeling (SEM), hierarchical moderation analysis and bootstrapping were used to test the study hypotheses.

Servant leadership was found to positively influence employee organizational identification and career satisfaction. Further, analysis revealed that LMX partially mediated the positive relationship between servant leadership and employee career satisfaction and fully mediated the positive relationship between servant leadership and organizational identification. However, although POS moderated the indirect relationship between servant leadership and employee’ career satisfaction, it did not moderate the indirect relationships between servant leadership and organizational identification.

This study provides insight into the nexus of servant leadership and organizational support in hospitality industry to foster the employee organizational identification and career satisfaction which are extremely needed for competitive advantage in hotel industry.

This study addresses recent calls for future researchers to investigate the important of servant leadership in the hospitality industry.