Search

Significant shifts in policy agenda and priorities may occur when exogenous and discontinuous macro-level events such as disease outbreaks, political transformations, and abrupt developments in visitor markets transpire. Such swings can be considerably challenging not only for policy-makers and decision-makers but also for stakeholders, especially when policy areas like employment, quality of life, housing, health, and education are weighed against growth and development considerations for hospitality, tourism, or other sectors. In extreme cases, policy swings can exacerbate social conflicts and cause commensurate disruption. Using the Hong Kong and Macao Special Administrative Regions of China as pooled comparative cases of policy-making, this study examines the content and structure of tourism-related and general policies across a 20-year pre-COVID period, how the different policy areas evolved and shifted in priorities, and the temporal correspondence of policy swings with factors, context, and conditions that likely precipitated them. The study adopts a mixed-methods approach combining large-scale text mining and content analysis of a large corpus of policy documents with qualitatively matching emergent macro policy shifts with relevant co-occurring events, aided by a theoretical framework generated from past studies. By unveiling the complicity of governance, social, and environmental conditions as well as external events with fluctuating policy priorities, the study dispels the static nature and fixed-planning perspectives of policy-setting, thereby advancing (a) a nascent framework by which policy-makers and decision-makers can adopt contingent and adaptable approaches to policy-making and (b) concrete principles for grasping the significance of tourism vis-à-vis public policies. [ABSTRACT FROM AUTHOR]

Different expressions of knowledge granularity need to be applied in different circumstances to reflect the average uncertainty of knowledge. In this study, we investigate the general expression of knowledge granularity based on a fuzzy relation matrix in the context where a knowledge structure is generated by a fuzzy relation. First, based on the orthogonal transformation principle of the relation matrix, we propose the structural equivalence relation and structural finer relation on the knowledge structure base. A partial order relation on the simplified knowledge structure base is then derived accordingly. Moreover, a specific knowledge structure base is utilized as an example to draw several diagrams to allow the intuitive interpretation of these relations. By combining these relations, the axiom properties of knowledge granularity are presented. Finally, a general expression is provided for fuzzy relation based knowledge granularity. We show that the selected function can be used to express knowledge granularity provided that it is reasonable. [ABSTRACT FROM AUTHOR]

Despite their separate research traditions, intelligence and executive functioning (EF) are both theoretically and empirically closely related to each other. Based on a subsample of 8- to 20-year-olds of the standardization and validation sample (N = 1540) of an internationally available instrument assessing both cognitive abilities, this study aimed at investigating a comprehensive structural model of intelligence and EF tasks and at gaining insight into whether this comprehensive model is applicable across sexes and age groups as well as to a subsample of participants with (borderline) intellectual disabilities (IQ ≤ 85, n = 255). The results of our exploratory factor analysis indicated one common EF factor that could be sufficiently integrated into the intelligence model within our confirmatory factor analyses. The results suggest that the EF factor can be added into the model as a sixth broad ability. The comprehensive model largely showed measurement invariance across sexes and age groups but did not converge within the subsample of participants with (borderline) intellectual disabilities. The results and implications are discussed in light of the current literature. [ABSTRACT FROM AUTHOR]

Despite their separate research traditions, intelligence and executive functioning (EF) are both theoretically and empirically closely related to each other. Based on a subsample of 8- to 20-year-olds of the standardization and validation sample (N = 1540) of an internationally available instrument assessing both cognitive abilities, this study aimed at investigating a comprehensive structural model of intelligence and EF tasks and at gaining insight into whether this comprehensive model is applicable across sexes and age groups as well as to a subsample of participants with (borderline) intellectual disabilities (IQ ≤ 85, n = 255). The results of our exploratory factor analysis indicated one common EF factor that could be sufficiently integrated into the intelligence model within our confirmatory factor analyses. The results suggest that the EF factor can be added into the model as a sixth broad ability. The comprehensive model largely showed measurement invariance across sexes and age groups but did not converge within the subsample of participants with (borderline) intellectual disabilities. The results and implications are discussed in light of the current literature.

A standard assumption in structural equation models with interaction terms is the normality of all the random constituents of the model. In applications, however, there may be predictors, disturbance terms of equations, or measurement errors, deviating from normality. The present paper investigates how deviation from normality affects the consistency of two alternative estimators; namely, the maximum likelihood (ML) and the method of moments (MM) estimators. The ML approach requires full specification of the distribution of observable variables while this is not required in the MM approach. It will be seen that while the MM estimator is insensitive to departures from normality of all the random constituents of the model not involved in the interaction terms, such deviation from normality distorts considerably the consistency property of the ML estimator. The paper provides analytical results showing the consistency of MM when using a proper selection of moments up to order three, and presents a Monte Carlo illustration showing how the consistency of the ML estimator breaks down when there is deviation from normality. It is concluded that for a variety of distributions of the data, the MM method gives consistent estimates while ML does not.

Software reuse focuses on developing software using reusability principles. Component Based Software Engineering (CBSE) is one of the approaches that support software reuse by utilizing reusable software components. Many approaches have been proposed in the literature to mine reusable components from existing object-oriented software. But, almost all of these approaches are dependent on the selection of third-party application software as clients. Therefore, this paper proposes a reusable component identification approach from the source-code of a singular object-oriented software system especially related to multimedia tools. The proposed approach is based on making use of two kinds of dependency relations among different software elements namely structural and lexical relations. These relations, help us to measure cohesion at the component level using two proposed cohesion metrics. The cohesion values are further used as objective functions in the multi-objective search-based evolutionary algorithm, namely NSGA-III to identify the set of reusable components as different clusters. The proposed approach is validated by applying it to six open source Java applications of different sizes and belonging to multimedia domains. [ABSTRACT FROM AUTHOR]