Search results

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

Thin cracks on the surface, such as those found in nuclear power plant concrete structures, are difficult to identify because they tend to be thin. This paper aims to design a novel segmentation network, called U-shaped contextual aggregation network (UCAN), for better recognition of weak cracks.

UCAN uses dilated convolutional layers with exponentially changing dilation rates to extract additional contextual features of thin cracks while preserving resolution. Furthermore, this paper has developed a topology-based loss function, called ℓ_cl Dice, which enhances the crack segmentation’s connectivity.

This paper generated five data sets with varying crack widths to evaluate the performance of multiple algorithms. The results show that the UCAN network proposed in this study achieves the highest F1-Score on thinner cracks. Additionally, training the UCAN network with the ℓ_cl Dice improves the F1-Scores compared to using the cross-entropy function alone. These findings demonstrate the effectiveness of the UCAN network and the value of incorporating the ℓ_cl Dice in crack segmentation tasks.

In this paper, an exponentially dilated convolutional layer is constructed to replace the commonly used pooling layer to improve the model receptive field. To address the challenge of preserving fracture connectivity segmentation, this paper introduces ℓ_cl Dice. This design enables UCAN to extract more contextual features while maintaining resolution, thus improving the crack segmentation performance. The proposed method is evaluated using extensive experiments where the results demonstrate the effectiveness of the algorithm.

The purpose of this paper is to examine whether and how chair-chief executive officer (CEO) generational difference is related to debt financing.

This paper adopts the pooled ordinary least squares and system generalized method of moments estimation procedures to analyze listed firms in Malaysia from 2013 to 2017.

The results reveal that chair-CEO generational difference is negatively associated with leverage. The evidence suggests that substantial age gaps between the chair and CEO precipitate cognitive conflicts, which lead to better monitoring and control. This results in better governance and less information asymmetry, causing firms to depend less on debt as a board monitoring mechanism. The findings provide support to the theory posited in this paper on the substitutability of chair-CEO generational difference and debt financing.

This is the first attempt to investigate the substitutability of chair-CEO generational difference and debt financing.

This study aims to investigate the effect of total external debt stock on economic growth of the East African Community (EAC) bloc.

The study applies balanced panel data for seven of the eight EAC member states, spanning the period from 2013 to 2022, and uses panel data models, i.e. pooled ordinary least squares, random and fixed effects models.

The findings reveal a significant positive correlation between total external debt stock and economic growth, supporting the economic theory that reasonable levels of borrowing can stimulate economic growth, particularly when funds are channeled into productive activities. However, the relationship between foreign direct investment and economic growth lacks statistical significance, indicating challenges in attracting sufficient investment for substantial growth within the EAC bloc. Trade openness shows a negative and statistically insignificant correlation with economic growth. Additionally, the study finds a positive and significant correlation between the unemployment rate and economic growth, while the inflation rate demonstrates a positive but statistically insignificant relationship with economic growth.

The study recommends improvements in debt management practices, enhancements in the business environment, infrastructure investments, a reassessment of trade policies and initiatives to stimulate job creation and SME development. More importantly, governments should focus on expanding the tax base in ways that stimulate growth, thereby reducing reliance on external debt.

This study is unique as it revisits the effect of external debt stock on economic growth following Somalia’s recent membership in EAC bloc.

COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. Considering the restricted and enclosed nature of prisons and closed environments and the prolonged and close contact between individuals, COVID-19 is more likely to have a higher incidence in these settings. This study aims to assess the prevalence of COVID-19 among prisoners.

Papers published in English from 2019 to July 7, 2023, were identified using relevant keywords such as prevalence, COVID-19 and prisoner in the following databases: PubMed/MEDLINE, Scopus and Google Scholar. For the meta-analysis of the prevalence, Cochrane’s Q statistics were calculated. A random effect model was used due to the heterogeneity in COVID-19 prevalence across included studies in the meta-analysis. All analyses were performed in STATA-13.

The pooled data presented a COVID-19 prevalence of 20% [95%CI: 0.13, 0.26] and 24% [95%CI: 0.07, 0.41], respectively, in studies that used PCR and antibody tests. Furthermore, two study designs, cross-sectional and cohort, were used. The results of the meta-analysis showed studies with cross-sectional and cohort designs reported 20% [95%CI: 0.11, 0.29] and 25% [95%CI: 0.13, 0.38], respectively.

Through more meticulous planning, it is feasible to reduce the number of individuals in prison cells, thereby preventing the further spread of COVID-19.

The research about the concept of Halal tourism has emerged recently. It becomes the vital factor in determining the tourists's satisfaction or their loyalty. Thus, this concept needs to be developed in certain areas because it still not acknowledged. The purpose of this study is to align the theoretical foundations of Halal tourism to conventional tourism paradigms. It investigates the value of Muslim tourists' perception in the context of Malaysian tourist destination. The six variables of Muslim tourist perceived value (MTPV) are examined such as quality, price, emotional, social, physical and non-physical attributes. The respondents of the research are a total of 205 Muslim tourists in Langkawi, Malaysia during October 2021 (Langkawi Tourism Bubble). The results indicated four (4) variables have impacts on tourist satisfaction. Practical implications will impact towards cognitive, affective and Islamic (i.e. Halal) values on tourist satisfaction. It was examined as one of the priotitize destination experience on tourist satisfaction. The findings provide Malaysian tourism with significant managerial implications. It also impacted the Halal tourism as a new approach specifically in the post COVID-19 era. Hence, in improving Muslim tourist satisfaction, destination marketers should consider the Halal tourism. Tour agencies also should scrutinize the product and services value including destination attributes that they offer. The destination's competitiveness will be strengthened with the right destination attraction, facilities, accommodation and activities that suit Muslim tourists.

By conducting a longitudinal study, this study aims to investigate how the role of hotel attributes in destination competitiveness changed through the stages of pre-, amid and recovery from the crisis.

First, the latent Dirichlet allocation method was used to identify hotel attributes from 15,137 online reviews, and then a sentiment analysis was performed to determine tourist satisfaction with the subject attributes. Second, separate asymmetric impact competitor analyses were conducted for the three stages of the crisis, and their results were compared with understand how the role of the hotel attributes changed throughout the crisis.

The results revealed that the impacts of hotel attributes on tourist satisfaction and destination competitiveness differed significantly at each stage of the crisis.

This research expands the existing literature by offering valuable insights by elucidating the changing characteristics of hotel attributes at each crisis stage. The results extend the body of knowledge in destination management by providing evidence on the validity of asymmetric impact competitor analysis.

To fully understand the impact of a crisis (e.g. COVID-19) on destination competitiveness with a focus on the hotel sector, this research conducted a longitudinal study that covers three stages of the crisis (i.e. pre-, amid and post-crisis). Moreover, unlike previous studies, this research considers the asymmetric relationships between service attributes and overall tourist satisfaction, as well as competitors’ information.

The aim of this article is to map the use of AI in the user experience (UX) design process. Disrupting the UX process by introducing novel digital tools such as artificial intelligence (AI) has the potential to improve efficiency and accuracy, while creating more innovative and creative solutions. Thus, understanding how AI can be leveraged for UX has important research and practical implications.

This article builds on a systematic literature review approach and aims to understand how AI is used in UX design today, as well as uncover some prominent themes for future research. Through a process of selection and filtering, 46 research articles are analysed, with findings synthesized based on a user-centred design and development process.

The authors’ analysis shows how AI is leveraged in the UX design process at different key areas. Namely, these include understanding the context of use, uncovering user requirements, aiding solution design, and evaluating design, and for assisting development of solutions. The authors also highlight the ways in which AI is changing the UX design process through illustrative examples.

While there is increased interest in the use of AI in organizations, there is still limited work on how AI can be introduced into processes that depend heavily on human creativity and input. Thus, the authors show the ways in which AI can enhance such activities and assume tasks that have been typically performed by humans.

The number of university students seeking mental health support is rapidly increasing. To provide additional psychological support to students accessing well-being services, this study aimed to pilot a mental health Web application (app) called Orpheus®.

Guided by student consultations, a multi-methods approach was adopted, including an examination of in-app data, chart reviews of routinely collected student information and interviews with mental health practitioners. Usage data were analysed descriptively. Changes in mental health outcomes were examined using means, standard deviations and reliable change indices for anxiety and depression scores. Inductive and deductive thematic analysis was conducted on qualitative data from staff interviews and student feedback.

A total of 26 students registered an account with 39 completed app visits. On 37 of the 39 (94.9%) occasions, students reported reductions in the intensity of unwanted negative emotions. Statistically significant reductions in the average pooled anxiety and depression scores were observed. Of the 15 students who completed pre- and post-routine outcome measures, between 20% and 60% showed reliable and meaningful symptom improvements. Students reported that the app was helpful and easy to set up and use, with no adverse events. Practitioners highlighted barriers and facilitators related to the technology features, situational contexts and individual differences.

Integrating Orpheus in real-world settings resulted in promising implementation processes, potential for future uptake and positive outcomes. However, ongoing research, staff training and app testing are needed to further improve the implementation processes for digital mental health interventions.

The present study reviews the theoretical and empirical literature about the significance of international migrants' remittance to empirically analyse the effect of remittance on the productivity growth of developing countries using a panel dataset from 1991 to 2021.

The study utilised the data envelopment analysis (DEA)-based Malmquist Productivity Index (MPI) to measure nationwide production efficiencies. It first performed a unit root test, cointegration test and pool mean group autoregressive distributed lag (PMG-ARDL) technique. To assess the robustness of the findings, the study also uses dynamic ordinary least squares (DOLS) and fully modified OLS (FMOLS) estimators.

The results demonstrated that remittances are a significant source of funding that promotes innovation [i.e. technological progress (TEC)] and hastens the country's total factor productivity (TFP) growth. However, the study needed to have established the effect of inward remittances on the nation's technical efficiency (EFF).

As remittances encourage innovation and TFP growth (TFPG), the concerned governments must create favourable and enabling economic environments to increase remittance inflows, which will have far-reaching growth repercussions.

The present study emphasises the connection between remittances and productivity growth, the disintegration of TFP, advanced econometric techniques and contribution to research policy. Despite prior literature exploring the effect of remittances on economic growth, a dearth of literature exists on how remittances affect a country's productivity. The output-based MPI methodology used in this study offered a nuanced understanding of how remittances affect many facets of productivity growth in developing nations.