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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.

Confidence intervals play a crucial role in economics and finance, providing a credible range of values for an unknown parameter along with a corresponding level of certainty. Their applications encompass economic forecasting, market research, financial forecasting, econometric analysis, policy analysis, financial reporting, investment decision-making, credit risk assessment and consumer confidence surveys. Signal-to-noise ratio (SNR) finds applications in economics and finance across various domains such as economic forecasting, financial modeling, market analysis and risk assessment. A high SNR indicates a robust and dependable signal, simplifying the process of making well-informed decisions. On the other hand, a low SNR indicates a weak signal that could be obscured by noise, so decision-making procedures need to take this into serious consideration. This research focuses on the development of confidence intervals for functions derived from the SNR and explores their application in the fields of economics and finance.

The construction of the confidence intervals involved the application of various methodologies. For the SNR, confidence intervals were formed using the generalized confidence interval (GCI), large sample and Bayesian approaches. The difference between SNRs was estimated through the GCI, large sample, method of variance estimates recovery (MOVER), parametric bootstrap and Bayesian approaches. Additionally, confidence intervals for the common SNR were constructed using the GCI, adjusted MOVER, computational and Bayesian approaches. The performance of these confidence intervals was assessed using coverage probability and average length, evaluated through Monte Carlo simulation.

The GCI approach demonstrated superior performance over other approaches in terms of both coverage probability and average length for the SNR and the difference between SNRs. Hence, employing the GCI approach is advised for constructing confidence intervals for these parameters. As for the common SNR, the Bayesian approach exhibited the shortest average length. Consequently, the Bayesian approach is recommended for constructing confidence intervals for the common SNR.

This research presents confidence intervals for functions of the SNR to assess SNR estimation in the fields of economics and finance.

The digital contents (d-contents) are vulnerable to various threats either natural or manmade. Digital preservation is the plethora of a wide array of strategies necessary for the long-term preservation of digital objects. This study was carried out to assess the digital preservation practices for information resources in university libraries of Pakistan.

A quantitative survey based on a structured questionnaire was carried out to conduct the study. The questionnaire containing two sets of strategies (general and technical) was distributed amongst the whole population and received 90% response rate.

Overall, progressive implementation of general digital preservation practices was noted in these libraries like checking the digital collections for viruses, keeping the digital media in fire/water/theft proof locations, restricting unauthorized access, maintaining ideal humidity and temperature, and checking the digital media for functionality. Amongst the technical practices, only replication was in practice at a progressive rate, followed by metadata recording and media refreshing that was sometimes practiced in these libraries. The other technical practices were rarely or never practiced in these libraries. Significant variances in general and technical digital preservation practices were noted based on their physical locations (regional distribution).

The study contributes a comprehensive set of digital preservation practices divided into general and technical types to conduct similar studies in other parts of the world.

The findings stress the need for national and institutional policies, funding streams and skill enhancement of library staff.

The study fills the literature gap and contributes a comprehensive set of digital preservation practices divided into general and technical types to conduct similar studies in other parts of the world.

The peer-review history for this article is available at: https://publons.com/publon/10.1108/OIR-02-2023-0074

Natural disasters profoundly influence agricultural trade sustainability. This study investigates the effects of natural disasters on agricultural production imports in China within 2002 and 2018. This exploration estimates the mediating role of transportation infrastructure and agriculture value-added and the moderating role of government effectiveness and diplomatic relations.

This investigation uses Probit, Logit, Cloglog and Ordinary Least Squares (OLS) models.

The results confirm the mediating role of transportation infrastructure and agriculture value-added and the moderating role of government effectiveness and diplomatic relations in China. According to the findings, natural disasters in trading partners heighten the risk to the agricultural imports. This risk raises, if disasters damage overall agricultural yield or transportation infrastructure. Moreover, governments’ effective response or diplomatic ties with China mitigate the risk. Finally, the effect of disasters varies by the developmental status of the country involved, with events in developed nations posing a greater risk to China’s imports than those in developing nations.

China should devise an early warning system to protect its agricultural imports by using advanced technologies such as data analytics, remote sensing and artificial intelligence. In addition, it can leverage this system by improving its collaboration with trading partners, involvement in international forums and agreement for mutual support in crisis.

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.

As many businesses faced economic disruption due to the Covid-19 pandemic and sought financial relief, existing bank relationships became critical to getting a loan. This study examines factors associated with the development of personal relationships of rural small businesses with community bank representatives.

We applied a mixed-method approach. We employed descriptive statistics, principal factor analysis and logistic regression for data analysis. We distributed an online survey to rural small businesses in five states in the United States. Key informant interviews with community bank representatives supplemented the survey results.

A business owner’s trust in a banker was positively associated with the establishment of a business–bank relationship. However, an analysis of individual trust’s components revealed that the nature of trust is complex, and a failure of one or more components may lead to decreased trustworthiness in a banker. Small businesses that preferred personal communication with a bank were more inclined to relationship banking.

Due to the relatively small sample size and cross-sectional data, our results may not be conclusive but should be viewed as preliminary and as suggestions for future research. Bankers should be aware of the importance of trust for small business owners and of the actions that lead to increased trustworthiness.

The study extends the existing knowledge on the business–bank relationship by focusing mainly on social (instead of economic) factors associated with the establishment of the business–bank relationship in times of crisis and high uncertainty.

The purpose of this chapter is to cover banking efficiency using the concept of the Meta frontier function and to study group and subgroup differences in the production technology. This study estimates the technical efficiency (TE) and technology gap ratios (TGRs) for banks in Islamic countries. Using the assumption of the convex hull of the Meta frontier production set using the virtual Meta frontier within the nonparametric approach as presented by Battese and Rao (2002), Battese et al. (2004), and O'Donnell et al. (2007, 2008) and after relaxing this assumption, the study investigates if there is a significant difference between these two methods. To overcome the deterministic criterion addressed to nonparametric approach, the bootstrapping technique has been applied. The first part of this chapter covers the analytical framework necessary for the definition of a Meta frontier function and its estimation using nonparametric data envelopment analysis (DEA) in the case where we impose the assumption of the convex production set and follows in the case of relaxation of this assumption. Then we estimated the TE and the TGR in concave and nonconcave Meta frontier cases by applying the Bootstrap-DEA approach. The empirical part will be reserved for highlighting these methods on data bank to study the technical and technological performance level and prove if there is a difference between the two methods. Three groups of banks namely commercial, investment, and Islamic banks in 17 Islamic countries over a period of 16 years between 1996 and 2011 are used.

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.