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This paper aims to shed light on how the composition of future blended learning (BL) courses can be changed to provide students with quality academic learning experiences. The model suggested in this study will guide instructors on how to design their course learning outcomes to ensure effective delivery.

The new model has been developed by combining Bloom's taxonomy and Carman's model. Later, a new framework entitled “PATHCO” based on an extensive literature review is applied to enhance the quality of all five components of Carman's model.

The PATHCO conceptual framework has been developed to ensure quality in the five main teaching and learning factors. This framework covers criteria like pedagogical, assessments, technical, health care and organizational. Further research is required to broaden the main elements of the suggested framework and to validate this research through a case study.

The COVID-19 pandemic has transformed the landscape of the education sector by encouraging an extensive acceptance of technology-enhanced learning and teaching. Blended learning (BL) has become the most appropriate medium to deliver online learning (OL). However, educators and students have reported dissatisfaction with the BL mode of delivery. To address this dissatisfaction, this study outlines, using the PATHCO model, all the essential building blocks which are required to find the right blend of both face-to-face and online components.

Carbon dioxide emissions affect the environment, presenting major implications for sustainable development and consequently model climate change policies. The main aim of the paper is to highlight the factors leading to CO2 emissions in Latin America.

The analysis was performed using data for 1990–2020 and panel regression and STATA software.

The results highlighted that the variables have significantly influence CO2 emissions in case of the countries in the sample.

The novelty of the paper consists in using all financial inflows of together (foreign direct investment, official development assistance and remittences), Latin America heavily in-flowed with remittances from the USA. Since Latin America is enriched with forest areas, the authors also covered this variable in the estimations. Urbanization and transportation are induced by remittance inflows, thus wellbeing was incorporated in the model. The conclusion of the study demonstrates the need for complex measures involving public-private partnerships, public awareness of the need for energy transition and the involvement of foreign-owned companies that must not only pursue their own interests but also generate positive economic, environmental, and social externalities in host countries.

This study aims to capture how the association between a multichannel relational communication strategy (MRCS) and customer performance is contingent upon such customer performance (low- versus high-performance customers) and to reconcile past contradictory results in this marketing-related topic. To this end, the authors propose and validate the method of quantile regression as an unconventional, yet effective, means to proceed to that reconciliation.

This study collected data from 4,934 customers of a private pension fund firm and accounted for both firm- and customer-initiated relational communication channels (RCCs) and for customer lifetime value (CLV). This study estimated a generalized linear model and then a quantile regression model was used to account for customer performance heterogeneity.

This study finds that specific RCCs present different levels of association with performance for low- versus high-performance customers, where outcome customer performance is the dependent variable. For example, the relation between firm-initiated communication (FIC) and performance is stronger for low-CLV customers, whereas the relation between customer-initiated communication (CIC) and performance is increasingly stronger for high-CLV customers but not for low-CLV ones. This study also finds that combining different forms of FIC can result in a negative association with customer performance, especially for low-CLV customers.

The authors tested the conceptual model in one single firm in the specific context of financial services and with cross-sectional data, so there should be caution when extrapolating this study’s findings.

This study offers nuanced and precise managerial insights on recommended resource allocation along with relational communication efforts, showing how managers can benefit from adopting a differentiated-customer performance approach when designing their MRCS.

This study provides an overview of the state of the art of MRCS, proposes a contingency analysis of the relationship between MRCS and performance based on customer performance heterogeneity and suggests the quantile method to perform such analysis and help reconcile past contradictory findings. This study shows how the association between RCCs and CLV varies across the conditional quantiles of the distribution of customer performance. This study also addresses a recent call for a more holistic perspective on the relationships between independent and dependent variables.

Focusing on small- and medium-sized enterprises (SMEs), which are characterized by resource restrictions, this paper aims to explore consumer segment profiles by considering demographic, personality and creativity traits to determine whether consumers with different profiles exhibit distinct affective reactions to different logo design types (organic, cultural and abstract).

This exploratory study incorporates recent methodological developments, such as the novel response style correction method, to account for response style effects in evaluations of affect toward logo design. In separate analyses, respondents are segmented according to response style–corrected logo affect and personality and creativity items. The segmentation analysis relies on reduced k-means, a joint dimension and cluster analysis method, which accounts for dependencies between items while maximizing between-cluster variability. A total of 866 respondents from the Iberian Peninsula (Portugal: n = 543; Spain: n = 323) participated.

Based on a study using unknown logos (proxy for lower levels of budget communication, characteristics of SMEs), results reveal that there are three segments of consumers based on their affective response toward logo design: logo design insensitives, cultural logo dislikers and organic logo lovers. These segments are associated with different personality traits, creativity and biological sex (although biological sex is not a discriminant variable).

The decision not to control logos by color, to increase external validity, could limit the study’s internal validity if this aspect interacts with relevant study variables. Nevertheless, the empirical evidence can be used to further test associations between consumer profiles and responses to logo design.

Findings highlight the relevance of considering complex profile segments, combining demographics, psychographics and creativity to predict affective consumer responses to brand logo design. This research provides guidelines for SMEs when choosing or modifying their logo design to appeal to different consumer segments.

This study provides managers of SMEs (less present nowadays in empirical studies) with evidence suggesting that complex customer profiles help to understand differences in affective responses to natural logo designs. Furthermore, it relies on the use of a novel methodological development that improves the accuracy of the exploratory study developed.

This is the first article to apply a machine learning approach to the analysis of time on market on real estate markets.

The random survival forest approach is introduced to the real estate market. The most important predictors of time on market are revealed and it is analyzed how the survival probability of residential rental apartments responds to these major characteristics.

Results show that price, living area, construction year, year of listing and the distances to the next hairdresser, bakery and city center have the greatest impact on the marketing time of residential apartments. The time on market for an apartment in Munich is lowest at a price of 750 € per month, an area of 60 m2, built in 1985 and is in a range of 200–400 meters from the important amenities.

The findings might be interesting for private and institutional investors to derive real estate investment decisions and implications for portfolio management strategies and ultimately to minimize cash-flow failure.

Although machine learning algorithms have been applied frequently on the real estate market for the analysis of prices, its application for examining time on market is completely novel. This is the first paper to apply a machine learning approach to survival analysis on the real estate market.

This paper proposes a model for detecting unexpected examination scores based on past scores, current daily efforts and trend in the current score of individual students. The detection is performed soon after the current examination is completed, which helps take immediate action to improve the ability of students before the commencement of daily assessments during the next semester.

The scores of past examinations and current daily assessments are analyzed using a combination of an ANOVA, a principal component analysis and a multiple regression analysis. A case study is conducted using the assessment scores of secondary-level students of an international school in Japan.

The score for the current examination is predicted based on past scores, current daily efforts and trend in the current score. A lower control limit for detecting unexpected scores is derived based on the predicted score. The actual score, which is below the lower control limit, is recognized as an unexpected score. This case study verifies the effectiveness of the combinatorial usage of data in detecting unexpected scores.

Unlike previous studies that utilize attribute and background data to predict student scores, this study utilizes a combination of past examination scores, current daily efforts for related subjects and trend in the current score.

In times of strong global competition and worldwide economic downturn, there is an imperative need for public services organizations to reform and improve their quality. These organizations can base their improvement efforts on Lean philosophy. The purpose of this study is to assess the employees’ perceived degree of adoption of Lean principles by public services organizations in Greece. Determining the differences in the perceptions of groups of employees with regard to the adoption of Lean principles by public organizations is also an aim of the study.

A questionnaire survey was published online, inviting employees of Greek public services organizations to assess the degree of Lean adoption by their organizations, specified on the basis of general principles. A total of 1,022 employees completed the structured questionnaire. Descriptive statistics were applied to assess the degree of adoption of Lean principles by public organizations. The nonparametric Mann–Whitney U Test and Kruskal–Wallis Test were also applied to determine whether there are statistically significant differences in the perceptions of groups of employees with regard to the adoption of Lean principles by public organizations.

According to the perceptions of employees, Greek public organizations adopt Lean principles to a high extent. However, there is room for further improvement in the degree to which Lean is adopted. Statistically significant differences are observed in the perceptions of groups of employees from different sized organizations, hierarchical levels, skill sets and service subsectors, with regard to the degree of adoption of Lean principles by their organizations.

The employees of the public sector who were invited to respond to the survey through social media, the subjective nature of the data collected and the fact that this is a country-specific study constitute the main limitations of the present study, based on which future studies can be designed.

By determining the strong and weak points of the adoption of Lean principles by Greek public services organizations, suitable managerial initiatives can be undertaken by these organizations to fully adopt Lean, eliminate waste and enhance quality management.

Understanding and improving the current status of the adoption of Lean principles by Greek public organizations will influence the services provided to the citizens in terms of time, quality and delivery.

To the best of the authors’ knowledge, this is the first study which provides insights, based on employees’ perceptions, into the adoption of Lean by the public services sector.

A growing body of literature focuses on crime hotspots; however, less is known about the spatial distribution of crime at mass transit systems, and even less is known about trajectory patterns of hotspots in non-English-speaking countries.

The spatiotemporal behaviour of 1,494 crimes reported to the Hong Kong’s Railway Police District across a two-year period was examined in this study. Crime harm weights were then applied to offences to estimate the distribution of crime severity across the transit system. Descriptive statistics are used to understand the temporal and spatial trends, and k-means longitudinal clustering are used to examine the developmental trajectories of crime in train stations over time.

Analyses suggest that 15.2% and 8.8% of stations accounted for 50% of all counted crime and crime harm scores, respectively, indicating the predictability of crime and harm to occur at certain stations but not others. Offending persists consistently, with low, moderate and high counts and harm stations remaining the same over time.

These findings suggest that more localised crime control initiatives are required to target crime effectively.

This is one of the only studies focusing on hotspots and harmspots in the mass transit system.

This study explored whether the critical incident management systems (CIMS) model can predict the EMS performance in the COVID-19 context. Past research has established the significance of early detection and response (ER) in the context of Ebola virus disease (EVD), prompting a question of whether the model can also be helpful in the COVID-19 context. Consequently, the authors assessed whether ER influences the impact of communication capacity (CC), reliable information channel (RC) and environment (EN) on COVID-19 EMS performance. Assessing these relationships will advance emerging infectious disease (EID) preparedness.

The authors employed standardized measurement instruments of the CIMS model (CC, ER, RC and EN) to predict the performance of COVID-19 EMS using structural equation modeling (SEM) in a study of 313 participants from frontline responders.

The results show that the relationship of ER and EN with COVID-19 EMS performance is positive, while that of EN on CC is negative. The relationship between EN and COVID-19 EMS performance was insignificant. Contrary to the hypothesis, CC was negatively significant to COVID-19 EMS performance due to poor communication capacities.

The authors acknowledge some limitations due to challenges faced in this study. First, Data collection was a significant limitation as these questionnaires were built and distributed in June 2020, but the response time was prolonged due to the recurring nature of the pandemic. The authors had wanted to implore the inputs of all stakeholders, and efforts were made to reach out to various Ministry of Health, the local CDC and related agencies in the region via repeated emails explaining the purpose of the study to no avail. The study finally used the frontline workers as the respondents. The authors used international students from various countries as the representatives to reach out to their countries' frontline workers. Second, since the study was only partially supported using the CIMS model, future studies may combine the CIMS model with other models or theories. Subsequent research reassesses this outcome in other contexts or regions. Consequently, further research can explore how CC can be improved with COVID-19 and another future EID in the region. This may improve the COVID-19 EMS performance, thereby expanding the lesson learned from the pandemic and sustaining public health EID response. Additionally, other authors may combine the CIMS model with other emergency management models or theories to establish a fully supported theoretical model in the context of COVID-19.

The findings have practical implications for incident managers, local CDCs, governments, international organizations and scholars. The outcome of the study might inform these stakeholders on future direction and contribution to EID preparedness. This study unfolds the impact of lessons learned in the region demonstrated by moderating early detection and responses with other constructs to achieve COVID-19 EMS performance. The findings reveal that countries that experienced the 2013–2016 Ebola outbreak, were not necessarily more prepared for an epidemic or pandemic, judging by the negative moderating impact of early detection and response. However, these experiences provide a foundation for the fight against COVID-19. There is a need for localized plans tailored to each country's situation, resources, culture and lifestyle. The localized plan will be to mitigate and prevent an unsustainable EID management system, post-epidemic fund withdrawals and governance. This plan might be more adaptable and sustainable for the local health system when international interventions are withdrawn after an epidemic. Public health EID plans must be adapted to each country's unique situation to ensure sustainability and constantly improve EID management of epidemics and pandemics in emergency response. The high to moderate importation risk in African countries shows Africa's largest window of vulnerability to be West Africa (Gilbert et al., 2020). Therefore, they should be in the spotlight for heightened assistance towards the preparedness and response for a future pandemic like COVID-19. The West African region has a low capacity to manage the health emergency to match the population capacities. The COVID-19 outbreak in West Africa undoubtedly inflicted many disruptions in most countries' economic, social and environmental circumstances. The region's unique challenges observed in this study with CC and reliable information channels as being negatively significant highlight the poor maintenance culture and weak institutions due to brain drain and inadequate training and monitoring. This outcome practically informs West African stakeholders and governments on aspects to indulge when trying to improve emergency preparedness as the outcomes from other regions might not be applicable.

This study explored the relevance of the CIMS model in the context of the COVID-19 pandemic, revealing different patterns of influence on COVID-19 EMS performance. In contrast to the extant literature on EVD, the authors found the moderating effects of ER in the COVID-19 context. Thus, the authors contribute to the COVID-19 EMS performance domain by developing a context-driven EMS model. The authors discuss the theoretical and practical implications.

By examining the impact of product innovation on abnormal financial returns following the launch of new products, this study aims to test the explanatory power of a new compound measure of product innovativeness (Ganbaatar and Douglas, 2019).

It is a longitudinal study in which the authors used the compound product innovativeness score (CPIS) for the first time to measure product innovativeness. The abnormal financial returns are estimated through the event study design, where four different models are used. Artificial neural network analysis is done to determine the impact of the CPIS on abnormal returns by utilising a hexic polynomial regression model.

The authors find effect sizes that substantially exceed practically significant levels and that the CPIS explain 65% of the variance in the firm’s abnormal returns in market valuation. Moreover, new-to-the-market novelty predicts 83% of the variation, while new-to-the-firm (catch-up) innovation insignificantly impacts firm value.

This paper demonstrates how the CPIS, an objective and direct measure of product innovativeness, can be used to gain more insight into the innovation effect.

Implications for the business practice of this study include the necessity of relentless innovation by firms in contested differentiated markets, particularly where technological advance is ongoing. Larger and mature firms must practice corporate entrepreneurship to renew their products on a continuous basis to avoid slipping backwards in their markets. Innovation leadership, rather than following the leader, is also important to increase competitive advantage, given the result that innovation followship does not produce abnormal financial returns.

In this study, the authors focused on the effect of product innovativeness on firm performance. While the literature affirms a positive relationship between innovation and firm performance, the effect size of this relationship varies, due largely to the authors contend to simplistic measures of innovativeness. In this study, the authors adopt the relatively novel “compound” measure of product innovativeness (Ganbaatar and Douglas, 2019) to better encapsulate the nuances of both technical novelty and market novelty. This measure of product innovativeness is applicable to firms of all sizes but is more easily applied to entrepreneurial new ventures and SMEs, and it avoids the shortcomings of prior firm-level and subjective measures of innovativeness for both smaller and larger firms. Using a more effective analytical method (Artificial Neural Network), the authors investigated whether there is a “practically” significant effect size due to product innovation, which could be valuable for entrepreneurs in practice. The authors show that the CPIS measure can very effectively explain abnormalities in the stock market, exhibiting a moderate effect size and explaining 65% of the variation in abnormal returns.