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Comparative analyses in education science have traditionally focused on the category of geographic location as the comparative unit. However, comparison may involve many other units of analysis, such as culture, politics, curricula, education systems, social phenomena, and other categories of the lives of societies. Still, categories are inseparably linked to one or several geographic locations. Comparative approaches are often also dictated by the availability heuristic. Studying geographic units as the foci of comparative research is a necessary step for comparative presentation of the topic. According to Bray and Thomas, a researcher must always seek preliminary insight in the geographic unit to be analyzed before making the comparison. In social science research, a unit of analysis relates to the main object of the research, as it answers the question of “who” or “what” is going to be analyzed. The most common units of analysis are people, groups, organizations, artifacts or phenomena, and social interactions. Ragin and Amoroso have noted that comparative methods can be used to explain the commonness or diversity of results. This paper shows how comparative research can be approached in ways that have not been discussed, grounded in the historically variable understanding of the very term “comparison.” They are, for example, The Ogden-Richards triangle, The Porphyrian Tree, Classification strategies – Mill’s Canons, The chaos of the world – the order of science, Weber’s ideal types, Raymond Boudon’s formula, and the Möbius strip in comparativism.

Seaports seek to find innovative technological solutions to deal with the ever-increasing growth of transportation and increasing the intensity of competition through the use of emerging technology such as digital twin technology to improve the quality of their logistics operations. Despite the success of digital twins in many industries, there is still a lack of their application in the field of seaports where ports play a central role as part of global transportation chains. Seaports sustainability comprises three main aspects: the social aspect that encompasses more job opportunities, the economic aspect that enhances foreign trade, and the environmental aspect that refers to the process of managing and operating ports in a way that saves the environment. This chapter discusses how to apply digital twins’ technology on the imported Twenty equivalent foot unit (TEUs) taking into consideration the population growth, and the capacity of the storage area of the container terminals in an attempt to explore the impact on Kuwait’s seaports sustainability. The study provides a framework for capacity management in an attempt to initiate the next generation of smart seaports cities and consequently impact society, economy, and well-being in Kuwait and Gulf region. The results of the study showed that there is a strong correlation between population growth and imported TEUs growth during the essential stage of the study. The correlation factor was 0.97, and this correlation will support the prediction until Kuwait vision 2040.

Algorithmic trading has evolved beyond traditional methods by incorporating machine learning techniques to analyse extensive datasets. The integration of machine learning and ATS has helped in enhancing the decision-making process, leading to more accurate predictions of market trends, risk assessments, and optimal execution strategies. The opaque nature of artificial trading models can create challenges in understanding the decision-making process of these systems. This lack of clear understanding raises questions about accountability, and market participants lack transparency on whether movements are economic-driven or algorithmic trading strategies. The chapter explores the development of I-driven trading and Key Characteristics of Algorithmic Trading Systems. In conclusion, the integration of machine learning into capital markets represents a major shift in how investment decisions are made, risks are managed, and how markets operate independent artificial intelligence trading systems. Its increasing use highlights the need for careful ethical consideration, regulatory flexibility and ongoing monitoring.

This paper aims to unravel the puzzle that the United Kingdom’s high-quality government accounting and fiscal architecture is associated with low-quality outcomes, including poor productivity growth, high public debt, public services which do not meet citizen expectations and historically high levels of taxation. It contributes to public sector accounting research in the fields of fiscal transparency and governance.

This paper uses Miller and Power’s (2013) economization framework and Dunsire’s (1990) concept of collibration to explain why being a global leader in public sector accounting reform and in fiscal and monetary architecture has not protected the UK from weak governance. The intersection of economization’s roles of accounting with modes of government accounting clarifies the puzzle.

Whereas accruals government accounting contributes to fiscal transparency, this is not a sufficient condition for well-judged policy and its effective application. Collibration is the dominant mechanism for mediation in the fiscally centralized UK, but it has failed to deliver stable outcomes, in part because Parliament is limited in its ability to hold back inappropriate behaviour by the Executive. Subjectivization has disrupted adjudication because governments at all levels resist constraints on their behaviour, with unpredictable and often damaging consequences.

This paper provides insights through the combined lens of economization and modes of government accounting, demonstrating the practical value of this conceptualization. Although some causes for unsatisfactory outcomes are specific to the UK, there are cautions for accounting and fiscal reformers in other countries, such as Member States of the European Union.

This paper aims to present a predictive model approach to estimate the tensile behavior of polylactic acid (PLA) under uncertainty using the fused deposition modeling (FDM) and American Society for Testing and Materials (ASTM) D638’s Types I and II test standards.

The prediction approach combines artificial neural network (ANN) and finite element analysis (FEA), Monte Carlo simulation (MCS) and experimental testing for estimating tensile behavior for FDM considering uncertainties of input parameters. FEA with variance-based sensitivity analysis is used to quantify the impacts of uncertain variables, resulting in determining the significant variables for use in the ANN model. ANN surrogates FEA models of ASTM D638’s Types I and II standards to assess their prediction capabilities using MCS. The developed model is applied for testing the tensile behavior of PLA given probabilistic variables of geometry and material properties.

The results demonstrate that Type I is more appropriate than Type II for predicting tensile behavior under uncertainty. With a training accuracy of 98% and proven presence of overfitting, the tensile behavior can be successfully modeled using predictive methods that consider the probabilistic nature of input parameters. The proposed approach is generic and can be used for other testing standards, input parameters, materials and response variables.

Using the proposed predictive approach, to the best of the authors’ knowledge, the tensile behavior of PLA is predicted for the first time considering uncertainties of input parameters. Also, incorporating global sensitivity analysis for determining the most contributing parameters influencing the tensile behavior has not yet been studied for FDM. The use of only significant variables for FEA, ANN and MCS minimizes the computational effort, allowing to simulate more runs with reduced number of variables within acceptable time.

This research examined the effects of entrepreneurial networking, operationalised as network isomorphism and network social capital, on the sustainable growth of small enterprises and the role of innovation in the hypothesised relationship.

The study employed a survey design and the questionnaire method to collect data from 319 small enterprises which were selected via quota sampling from the membership lists of the Association of Ghana Industries and the Ghana Enterprises Agency. Data were analysed using the covariance-based structural equation modelling (CV SEM-Amos) technique.

The study found that most small enterprises participate in business networks, followed by social and political networks. Entrepreneurial networking has a significant positive influence on the sustainable growth of small enterprises, and the influence is strongly mediated by innovation. Compared with network isomorphism, the value derived from social networks has a higher significant effect on sustainable enterprise growth.

Small enterprises that tap into valuable network resources to drive internal innovation should experience sustainable growth, whereas those that do not leverage the power of entrepreneurial networking miss the opportunity to access critical resources for innovation and sustainable enterprise growth.

The paper extends beyond the resource-based view by integrating institutional and network-based theories to offer a unique “entrepreneurial network model.” By testing the model, the empirical findings provide unique comprehensive insights into network isomorphism and network social capital as entrepreneurial mechanisms which facilitate access to network resources for innovations and sustainable growth of small enterprises.

The purpose of this study is to propose an analytical framework for generating main path analysis (MPA) and demonstrate the process involved in identifying, analyzing the MPA on a citation network and empirically testing in the research field chromosome anomalies (CA).

The proposed methodological structure involves five phases of the process. Search path method is used to measure the weights of each citation link from a source vertex to a sink vertex. The key route local main path and global main path are generated to identify the knowledge diffusion trajectories and validated by cross-referencing with existing literature, co-citation analysis and centrality measures of social network analysis.

The empirical validation of this framework within CA research demonstrates its potential for tracing knowledge diffusion and technological development trajectories over three decades. This approach elucidates two major intellectual knowledge flows. The first key-route main path identified the primary diagnostic protocols. The second key-route main path revealed that cancer or carcinogenesis is identified as one of the mainstream of CA.

The limitations of the data and coverage period restrict the scope of this study. MPA was applied exclusively to the most influential sub network and disregarded other sub networks. MPA identified the seminal papers that provided a historical development in diagnostic protocol and their interconnectedness of disorders and diseases. This helps the researchers to develop targeted therapies and interventions, especially in cancer treatment.

Exploiting MPA on CA research provides valuable insights to stakeholders in developing evidence-based public health policies. This is crucial for preventing the birth of children with birth defects or genetic diseases, promoting public health and reducing the socioeconomic burden on a country through enhanced surveillance and prevention efforts.

The study suggests that in addition to traditional scientometrics measures, MPA can be used to trace the evolution of knowledge and technological advancements. It also highlights the role of social network analysis measures in extracting main paths.

This study aims to examine the interaction effects of chatbots’ language style and customers’ decision-making journey stage on customer’s service encounter satisfaction and the mediating role of customer perception of emotional support and informational support using the construal level theory and social support theory as conceptual frameworks.

This study used a scenario-based experiment with a 2 (chatbot’s language style: abstract language vs concrete language) × 2 (decision-making journey stage: informational stage vs transactional stage) between-subjects design.

Findings show that during the informational stage, chatbots that use abstract language style exert a strong influence on service encounter satisfaction through emotional support. During the transactional stage, chatbots that use concrete language style exert a strong impact on service encounter satisfaction through informational support.

Findings provide some suggestions for improving customer–chatbot interaction quality during online service encounters.

This study offers a novel perspective on customer interaction experience with chatbots by investigating the chatbot’s language styles at different decision-making journey stages.

This paper investigates how two characteristics of language, abstractness vs concreteness and narrativity, influence user engagement in communication exercises on innovation targeted to the general audience. The proposed conceptual model suggests that innovation fits well with more abstract language because of the association of innovation with imagination and distal construal. Moreover, communication of innovation may benefit from greater adherence to the narrativity arc, that is, early staging, increasing plot progression and climax optimal point. These effects are moderated by content variety and emotional tone, respectively.

Based on a Latent Dirichlet allocation (LDA) application on a sample of 3225 TED Talks transcripts, the authors identify 287 TED Talks on innovation, and then applied econometric analyses to test the hypotheses on the effects of abstractness vs concreteness and narrativity on engagement, and on the moderation effects of content variety and emotional tone.

The authors found that abstractness (vs concreteness) and narrativity have positive effects on engagement. These two effects are stronger with higher content variety and more positive emotional tone, respectively.

This paper extends the literature on communication of innovation, linguistics and text analysis by evaluating the roles of abstractness vs concreteness and narrativity in shaping appreciation of innovation.

This paper reports conceptual and empirical analyses on innovation dissemination through a popular medium – TED Talks – and applies modern text analysis algorithms to test hypotheses on the effects of two pivotal dimensions of language on user engagement.

Although online reviews have become a key source of information for consumer purchasing decisions, little is known about how the concreteness of language used in these reviews influences perceptions of deception. This study aims to address this important gap by drawing on psycholinguistic research and Language Expectancy Theory to examine how and when the concreteness of online reviews (abstract vs concrete) impacts consumers’ perceived deception.

Two scenario-based experiments were conducted to examine how the concreteness of online reviews (abstract vs concrete) influences consumers’ perceptions of deception, considering the mediating role of psychological distance to online reviews and the moderating effects of Machiavellianism (Mach) and reviewer identity disclosure.

Online reviews that include concrete language lead to lower perceived deception by reducing consumers’ psychological distance from the review. For consumers with higher levels of Mach, online reviews written in abstract (vs concrete) language result in higher perceived deception via psychological distance, while for consumers with lower Mach, online reviews written in concrete (vs abstract) language result in higher perceived deception via psychological distance.

To the best of the authors’ knowledge, this study is one of the first to highlight the relevance of linguistic style (i.e. concrete review vs abstract review) on consumers’ perceived deception toward online reviews in the context of e-commerce.

The framework enables managers of online retailing platforms to identify the most effective strategies to decrease consumers’ perceived deception via the appropriate utilize of linguistic styles of online reviews.

This study contributes to both theory and practice by deepening knowledge of how and when the concreteness of online reviews (abstract vs concrete) affects consumers’ perceived deception and by helping managers of online retailing platforms make the most effective\ strategies for reducing consumers’ perceived deception toward online reviews during online shopping.