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The retail sector has the largest energy consumption among commercial buildings in the U.S. Although previous studies explored benefits, barriers and solutions for implementing sustainability in various building sectors, research focused on retail facilities has been very scarce. This study aims to explore U.S. facilities managers’ perceptions of barriers that prevented the implementation of energy-efficiency practices in the retail sector. Their perceptions were compared by facility size and facilities management company’s business revenue.

An online survey was distributed to the members of the International Facility Management Association and the author's LinkedIn network. The survey responses were analyzed using descriptive statistical analysis and ANOVA.

Managers from large facilities, as opposed to those from small ones, significantly more agreed that the unavailability of building automation systems, a lack of professional writing skills and a lack of awareness of life cycle cost (LCC) were the barriers. Business revenue did not cause significantly different perceptions of the barriers except for a lack of awareness of LCC and a lack of support from upper management.

This study fills the research gap on energy efficiency in the retail sector by revealing U.S. facilities managers’ perceptions of the barriers to the implementation of energy-efficiency practices in retail stores. This novel study compares perceptions of the facilities managers by facility size and business revenue; this comparison has not been performed before. The study also identified several new barriers to the implementation of energy efficiency in the retail sector.

This study aims to introduce the model of implementation to run the smart production factories. The study also aims to investigate the Industry 4.0 technologies as enablers to deal with challenges in the way of implementation.

This contribution benefits from two teams of experts to evaluate the challenges and technologies of Industry 4.0. The Hanlon method is applied to evaluate, rank and prioritise the challenges which are initially scored by experts’ Team 1. Then, the adjacency matrix among enablers and challenges is extracted through the opinions of experts’ Team 2. The study also uses fuzzy cognitive map (FCM) to evaluate the real weights of technologies and challenges, rank and prioritise subsequently.

A total of 8 challenging obstacles and 24 key technologies have been evaluated. The findings reveals that recruit and retention of experienced managers, undefined return on investment and recruit and retention of multi-skilled workers are the most serious challenges in the way of implementing smart production factories. Furthermore, big data, IT-based management and Internet of Things are the top-ranked key enablers to face the challenges.

To the best of the authors’ knowledge, this study is one of the pioneering studies that uses Hanlon method to evaluate industrial challenges. Integrating Hanlon method and FCM leads to a comprehensive model of evaluation and ranking which is another novelty of this contribution. Although many research studies have been released to implement the smart factories, practical model of implementation for production factories is identified as a literature gap.

In this study, the central objective is to foresee stock market signals with the use of a proper structure to achieve the highest accuracy possible. For this purpose, three hybrid models have been developed for the stock markets which are a combination of support vector machine (SVM) with meta-heuristic algorithms of particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).All the analyses are technical and are based on the Japanese candlestick model.

Further as per the results achieved, the most suitable algorithm is chosen to anticipate sell and buy signals. Moreover, the authors have compared the results of the designed model validations in this study with basic models in three articles conducted in the past years. Therefore, SVM is examined by PSO. It is used as a classification agent to search the problem-solving space precisely and at a faster pace. With regards to the second model, SVM and ICA are tested to stock market timing, in a way that ICA is used as an optimization agent for the SVM parameters. At last, in the third model, SVM and GA are studied, where GA acts as an optimizer and feature selection agent.

As per the results, it is observed that all new models can predict accurately for only 6 days; however, in comparison with the confusion matrix results, it is observed that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the data for stock market of the years 2013–2021 were analyzed; the long length of timeframe makes the input data analysis challenging as they must be moderated with respect to the conditions where they have been changed.

In this study, two methods have been developed in a candlestick model; they are raw-based and signal-based approaches in which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

The proposed model has been aimed to predict stock market signals by designing an accurate model. In this sense, the stock market is analysed by the technical analysis of Japanese Candlestick, which is combined by the following meta heuristic algorithms: support vector machine (SVM), meta-heuristic algorithms, particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).

In addition, among the developed algorithms, the most effective one is chosen to determine probable sell and buy signals. Moreover, the authors have proposed comparative results to validate the designed model in this study with the same basic models of three articles in the past. Hence, PSO is used as a classification method to search the solution space absolutelyand with the high speed of running. In terms of the second model, SVM and ICA are examined by the time. Where the ICA is an improver for the SVM parameters. Finally, in the third model, SVM and GA are studied, where GA acts as optimizer and feature selection agent.

Results have been indicated that, the prediction accuracy of all new models are high for only six days, however, with respect to the confusion matrixes results, it is understood that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the authors to analyze the data the long length of time between the years 2013–2021, makes the input data analysis challenging. They must be changed with respect to the conditions.

In this study, two methods have been developed in a candlestick model, they are raw based and signal-based approaches which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

When a new logo is released, it does not have an established meaning in the mind of the viewer. As logos have become more highly scrutinized by consumers and critics, it has become more important to understand viewers’ initial responses to logos. While other studies have researched the impact of aesthetic choices on viewer reaction to logos, this study aims to understand the effect of the surrounding visual identity system when a new logo is introduced.

This study combines a content analysis of 335 posts on the logo review website Brand New with the voting data from their polls to understand how visual context correlates with a viewer’s initial response.

Increased amounts of visual context correlate to an improved response from viewers. Different types of context that can be presented – from logo variations and environmental examples to videos and animation – have varied effects.

When releasing a new logo, companies and organizations may receive a better response from viewers if they provide more visual context. Animations may also provide an improved response.

This study takes a novel approach to exploring viewer responses to logos by combining content analysis with voting data. While most studies use fictitious or abstract logo designs, this study uses actual logos and context to better understand viewer responses.

The purpose of this paper is to investigate the impact of customer-focus on small medium enterprise (SME) performance from the perspective of a resource-based view (RBV).

This research study implemented a survey strategy to gather data from 255 respondents on the registered list of Ghana Enterprise Agency (GEA) in the eastern region of Ghana. Scales used to gather data were operationalized from previous research studies. A structural equation modeling (SEM) path analysis was used to estimate the impact of customer-focus on the performance of SMEs.

The outcomes of this study indicate that customer-focus has a significant positive impact on SME performance, hence backing the current demand for investigating the distinct influence of customer-focus on SME performance. The results show that customer-focus has a positive and significant relationship with financial performance, customer performance, internal business process performance and learning and growth performance, thus supporting the literature on the positive impact of customer-focus on SME performance. Therefore, customer-focus determinants used in this study, including co-creation, networking ties, customer insight and artificial intelligence marketing (AIM), are critical to the optimization of SME performance.

Notwithstanding the importance of this research study mentioned earlier, the study has limitations. Notably, the sample size of this study can be increased to capture SME respondents in other geographical zones that were not included in this study. Future research studies may address how business environment conditions moderate the relationship between customer focus and performance, and also the cause-effect of the relationship between customer focus and business environment conditions on SME performance.

The practical implications consist of two main items. First, this study empowers SME owners and managers to develop a customer focus technique as a central strategic goal in their quest for SME performance optimization. Second, SME owners and managers should progressively exploit the four determinants of customer focus which include co-creation, networking ties, customer insight and (AIM in order to accrue important resources for effective utilization of their customer focus competences as a way to enhance their performance.

This study is targeted at the sound development of SMEs to bring about poverty alleviation and employment. Poverty, unemployment and poor living standards are recognized as vital social challenges in most emerging economies. The establishment of customer focus as an important strategic capability provides opportunities for SME survival, profitability and growth.

Generally, the findings of this research study provide a strong backing to RBV perspective and the proposition that customer-focus and its determinants (i.e. co-creation, networking ties, customer insight and AIM) should be acknowledged as a vital strategic resource for optimizing the performance of SMEs. This research study also provides new knowledge contribution to the present body of knowledge on customer-focus orientation and management literature, particularly in the context of an emerging economy.

Due to the high rate of failure of most crowdfunding projects, knowing the most essential factor to obtain funding success on the crowdfunding platform is of great importance for fund seekers on the crowdfunding platform. The purpose of this study is to explore crowdfunding success factors to know the most essential success factor for stakeholders of the crowdfunding platform to make the best decision when seeking funds on the crowdfunding platform. This study identified and ranked crowdfunding success factors for stakeholders of crowdfunding platforms. Sixteen factors were identified and categorized under five broad headings. These were; project ideas, target capital, track records, geographical proximity and equity.

To rank the identified crowdfunding success factors and subfactors, this study used the Multi-Objective Optimization Based on Ratio Analysis (MULTIMOORA) integrated with the Evaluation based on Distance from Average Solutions (EDAS).

Target capital ranked first among the five categories—while duration involved in raising funds ranked first among the sixteen subfactors. An approach for analyzing how each success factor enhances a crowdfunding campaign was developed in this study. This study provides valuable insight to fund seekers on the crowdfunding platform on how funding success can be achieved by knowing which factor to consider essential when seeking funds on the crowdfunding platform.

This is the first study to explore crowdfunding success factors using the MULTIMOORA-EDAS method. The use of this method will help fund seekers on the crowdfunding platform to know which crowdfunding success factor is essential, thereby aiding fund seekers to make the best decision when seeking funds on the crowdfunding platform. Also, this study is particularly helpful for business owners, platform operators and policymakers when deciding how to allocate resources, plan campaigns and implement regulations.

This study aimed to identify and describe how mentoring influences the mentor, by operationalising and specifying learning outcomes involved in mentoring.

This study used an action research approach, by uniting theory and practice to explore new ways of learning and evolve the field of practice in education. Thematic analysis was used to identify and organise patterns or themes that emerged from the data.

The results showed that mentoring changed the mentors' perspectives towards improved understanding, more flexibility and approval of other cultures. It seems that mentoring expanded the mentors' search for values, wishes and resources, including an awareness that our values, wishes and needs are more similar than different. Mentoring also seems to have improved the ability to reformulate, be flexible, strive to optimise user engagement and engage with people as they are, based on their own prerequisites.

The low number of participants means the results cannot be generalised, and voluntary participation may have led to more motivated involvement and positive results.

This study shows that mentoring has had an impact on students' development of intercultural competence and cultural sensitivity through regular meetings with individuals from a different cultural background. Mentoring seems to have revealed insights into underlying prejudices and changed perspectives towards better understanding, thus increased acceptance of other cultures.

Search for similar studies shows a lack of research that operationalises and specifies the learning outcomes that mentors gain from being a mentor.

The health of people living in prisons (PLP) frequently remains marginalised in national development discourse, particularly in resource-constrained settings like Ghana. This study aims to determine the prevalence of cardiovascular disease (CVD) risk factors among PLP at a prison facility in the Northern Region of Ghana.

A cross-sectional study involving 134 male persons in prison, aged 18–79 years, was conducted to assess their dietary habits, tobacco use, alcohol consumption, sleep behaviour and physical activity practices. Serum lipid profile, fasting blood glucose (FBG), blood pressure (BP) and body mass indices of participants were also measured.

Almost half (48.1%) of the participants had abnormal lipid levels. Those with FBG in the diabetes range (= 7.0 mmol/l) constituted 3.9%, while 16.7% were in the impaired FBG range (6.1–6.9 mmol/l). Participants with BP within the pre-hypertension range were 54.5%. The majority of participants (92%) had a low daily intake of fruits and vegetables. Few participants were active smokers (5%) and alcohol users (2%). The average sleep duration at night among the participants was 5.54 ± 2.07 h. The majority (74%) of the participants were sedentary. About a quarter of the participants (24.6%) had overweight/obesity.

This study highlights the CVD risks among PLP. Findings suggest the need for targeted interventions, such as dietary and lifestyle modification strategies, regular physical activity and routine screening for diabetes, dyslipidaemia and hypertension. These interventions within the prison space could significantly improve the cardiovascular health of PLP in resource-limited settings.