Search results

This study aims to disclose the antecedent factors for predicting support for cruise tourism in the Bahamas. It investigated the relationship between residents’ support for cruise tourism and the four indicators that were the positive/negative impact of cruise tourism on the community, perceived conflicts in sharing information and concerns about the COVID-19 pandemic.

Using 278 surveys of local residents near major cruise ports in the Bahamas, this study identified the salient variables in tourism impact and conflict factors. The survey questionnaire was adapted and developed from relevant studies and modified to suit the context of cruise tourism.

The results revealed that residents’ perceived conflict was insignificantly associated with their support for cruise tourism. In contrast, their concerns about COVID-19 and perceptions of the positive and negative cruise tourism impacts were statistically significant in predicting the likelihood of support for tourism.

This study suggests implications for enhancing the long-term growth of the cruise industry, which is vulnerable to environmental threats such as Covid-19.

National surveys reveal that sports fans exhibit greater support for athletes, sports teams and leagues endorsing social justice initiatives compared to the general population, highlighting the potential of sports for positive social impact. This study investigates whether such responses are influenced by systematic biases.

Replicating a Nielsen national survey, two experiments explore whether biases affect support for athletes' participation in the Black Lives Matter (BLM) movement. The study also examines partisan motivated reasoning as a factor driving sports fans' support for BLM.

While avid fans display stronger endorsement of BLM compared to causal/non-sports fans, evidence suggests that systematic biases distort these responses. When sport identity becomes salient, reported support for the BLM movement becomes inflated.

Researchers often employ self-report surveys to gauge audience perceptions of athlete activism or cause-related initiatives, particularly when assessing their impact. This study's findings indicate that this context is susceptible to SDB.

The study underscores the role of systematic biases in self-report surveys, particularly in socially desirable contexts. People tend to over-report “positive behavior,” leading survey participants to respond more favorably to questions that are socially desirable. Therefore, interpreting survey results with caution becomes essential when the research context is deemed socially (un)desirable. It is crucial for researchers to apply appropriate measures to identify and mitigate systematic response biases. The authors recommend that researchers adopt both procedural and statistical remedies to detect and reduce social desirability biases.

Despite ongoing research into archival metadata standards, digital archives are unable to effectively represent records in their appropriate contexts. This study aims to propose a knowledge graph that depicts the diverse relationships between heterogeneous digital archive entities.

This study introduces and describes a method for applying knowledge graphs to digital archives in a step-by-step manner. It examines archival metadata standards, such as Records in Context Ontology (RiC-O), for characterising digital records; explains the process of data refinement, enrichment and reconciliation with examples; and demonstrates the use of knowledge graphs constructed using semantic queries.

This study introduced the 97imf.kr archive as a knowledge graph, enabling meaningful exploration of relationships within the archive’s records. This approach facilitated comprehensive record descriptions about different record entities. Applying archival ontologies with general-purpose vocabularies to digital records was advised to enhance metadata coherence and semantic search.

Most digital archives serviced in Korea are limited in the proper use of archival metadata standards. The contribution of this study is to propose a practical application of knowledge graph technology for linking and exploring digital records. This study details the process of collecting raw data on archives, data preprocessing and data enrichment, and demonstrates how to build a knowledge graph connected to external data. In particular, the knowledge graph of RiC-O vocabulary, Wikidata and Schema.org vocabulary and the semantic query using it can be applied to supplement keyword search in conventional digital archives.

The paper develops a model for enhancing R&D productivity for Indian public funded laboratories. The paper utilizes the productivity data of five Council of Scientific and Industrial Research (CSIR) laboratories for analysis and to form the constructs of the model.

The weighted average method was employed for analyzing the rankings of survey respondents pertaining to the significant measures enhancing R&D involvement of researchers and significant non-R&D jobs. The authors have proposed a model of productivity. Various individual, organizational and environmental constructs related to the researchers working in the CSIR laboratories have been outlined that can enhance R&D productivity of researchers in Indian R&D laboratories. Partial Least Squares-Structural Equation Modeling (PLS-SEM) was used to find the predictability of the productivity model.

The organizational factors have a crucial role in enhancing the R&D outputs of CSIR laboratories. The R&D productivity of researchers can be improved through implementing the constructs of the proposed model of productivity.

The R&D productivity model can be adapted by the R&D laboratories to enhance researchers’ R&D involvement, increased R&D outputs and achieving self-sustenance in long run.

The R&D laboratories can initiate exercises to explore the most relevant factors and measures to enhance R&D productivity of their researchers. The constructs of the model can function as a guideline to introduce the most preferable research policies in the laboratory for overall mutual growth of laboratory and the researchers.

Hardly any studies have been found that have focused on finding the measures of enhancing R&D involvement of researchers and the influence of significant time-intensive jobs on researchers’ productivity.

This paper aims to examine how the diversity of educational levels within bank boards influences the efficiency and stability of banks operating in the Middle East and North Africa (MENA) region. Unlike previous studies, this analysis also investigates the role of board gender diversity in moderating the relationship between board educational level diversity and bank efficiency and financial stability in MENA.

In this study, a sample of 77 banks in the MENA region spanning the years 2011 to 2018 is used. The relationship between the presence of highly educated directors on the board, bank efficiency and stability is assessed using the ordinary least squares method. Additionally, the authors use the Generalized Method of Moments technique to correct endogeneity problem.

This study establishes a positive association between the presence of directors with advanced educational backgrounds on bank boards and bank efficiency and stability. Furthermore, the inclusion of women on the board strengthens this relationship.

These findings have important implications for policymakers and regulators in the MENA region, suggesting that promoting diversity policies that encourage the participation of highly educated directors on bank boards can contribute to enhanced efficiency and financial stability. Policymakers may also consider implementing quotas or guidelines to improve gender diversity in board appointments, thereby fostering bank performance in the region.

This study stands out for its innovation and distinctiveness, as it delves into the connection between board educational level diversity and bank efficiency in the MENA region. Notably, it surpasses previous research by investigating the moderating role of board gender diversity, thus offering valuable insights into the complex interplay between these two facets of board diversity. This contribution enriches the existing literature by providing novel perspectives on board composition dynamics and its influence on bank efficiency and stability.

Compared with traditional methods relying on manual teaching or system modeling, data-driven learning methods, such as deep reinforcement learning and imitation learning, show more promising potential to cope with the challenges brought by increasingly complex tasks and environments, which have become the hot research topic in the field of robot skill learning. However, the contradiction between the difficulty of collecting robot–environment interaction data and the low data efficiency causes all these methods to face a serious data dilemma, which has become one of the key issues restricting their development. Therefore, this paper aims to comprehensively sort out and analyze the cause and solutions for the data dilemma in robot skill learning.

First, this review analyzes the causes of the data dilemma based on the classification and comparison of data-driven methods for robot skill learning; Then, the existing methods used to solve the data dilemma are introduced in detail. Finally, this review discusses the remaining open challenges and promising research topics for solving the data dilemma in the future.

This review shows that simulation–reality combination, state representation learning and knowledge sharing are crucial for overcoming the data dilemma of robot skill learning.

To the best of the authors’ knowledge, there are no surveys that systematically and comprehensively sort out and analyze the data dilemma in robot skill learning in the existing literature. It is hoped that this review can be helpful to better address the data dilemma in robot skill learning in the future.

This study aims to identify the impact of drivers of citizens’ e-loyalty on e-government services. This study focused on the impact of e-service quality (e-SQ) on e-loyalty, mediated by e-trust. In addition, the study examined the moderating role of system anxiety.

To accomplish the study’s objectives, a self-administered questionnaire was created to collect data, and the sample size was chosen to align with the requirements of the structural equation modeling (SEM) approach. Out of the distributed questionnaires, 532 were deemed valid and suitable for analysis in this research. Data screening was performed, and no questionnaires were excluded from the analysis.

The study findings underscore the significance of enhancing e-SQ for improved trust, satisfaction and engagement in e-government initiatives. Decision-makers should prioritize streamlined processes, user-friendly interfaces and responsive support. Crucial elements for fostering trust include transparency, accountability and data security. Personalized services, citizen engagement and continuous feedback evaluation contribute to citizen satisfaction and loyalty. Addressing system anxiety is vital through clear instructions and accessible support. Implementation of these recommendations is expected to lead to successful e-government initiatives and increased e-service adoption. The study highlights the importance of maintaining high e-SQ standards, trust-building measures and adopting a holistic approach for sustained positive user experiences in government e-services.

This study found a significant positive influence of e-SQ on e-loyalty showing a strong positive correlation between e-SQ, e-loyalty and e-service. Statistical analysis reported a significant positive mediating role of e-trust in the relationship between e-SQ and e-loyalty. Moreover, system anxiety exhibited a strong significant negative moderating role on the relationship between e-SQ and e-trust.

Practical implications of the study emphasize the importance of improving e-SQ, enhancing transparency, strengthening security measures, adopting user-centric design principles and continuously evaluating and improving e-services. By implementing these recommendations, decision-makers can foster trust, satisfaction and improve engagement and adoption of e-government initiatives in the Jordanian context as a developing country.

The study explores the factors influencing citizens’ loyalty to e-government services in Jordan, acknowledging the unique challenges faced by the country as a developing nation. It focuses on understanding these factors within the Jordanian context, where e-government initiatives are increasingly implemented to enhance public services and governance. The research investigates the mediating role of e-trust and the moderating effect of system anxiety, providing valuable insights into the drivers of citizens’ loyalty.