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The significant benefits associated with cruise tourism have mobilized port industry, as progressively, a large number of ports are developing cruise operations. Although increasing cruise traffic is a major goal for cruise ports, homeporting constitutes a strategic target of the majority of ports due to its greater economic benefits for both the port itself and its hinterland. The establishment of homeport traffic in a cruise port is subjected to a variety of port internal and external factors. Taking these into account, the paper aims at defining elements that affect the potential of a cruise port to become a homeport.

A sample of 47 Mediterranean ports is selected to form the basis for the implementation of an ordinal regression model which links the likelihood of ports to attract homeport traffic with seven explanatory variables which emerged from relevant literature and are split in the main categories of ports’ and hinterlands’ characteristics. To fit the model into the paper’s data, ports are divided into three categories based on their homeport cruise traffic.

The results of the empirical model signify that both internal and external factors affect the potential of a port to become a cruise homeport. Concerning the internal factors, adequate infrastructure allowing the facilitation of the last generation of cruise ships and the presence of a private enterprise in ports’ operation seems to foster homeport traffic. Additionally, efficiency in operations seems to be a crucial element. On the other hand, the connectivity of port’s; hinterlands, tourist infrastructure and the level of economic growth are proved to be the hinterlands’ elements which increase the likelihood of a port to attract additional homeport traffic.

The model forms a comprehensive evaluation basis for whether a cruise port should intensify its pursuit of homeport traffic, as the estimated coefficients could support port and local authorities to understand their competitive position against other ports and spot their strengths and weaknesses.

The paper contributes in the research dealing with the identification of crucial elements of homeporting from the port’s point of view. Although, it should be mentioned that previous efforts targeting on revealing the characteristics affecting the homeporting potential of ports mostly have been based on questionnaires and expert judgements or empirical models in which the total – and not the homeport traffic – was used as the dependent variable. With the proposed empirical model, home-porting choice analysis is transferred, on the one hand, from the stated preferences level to the revealed preferences level and, on the other hand, from an indirect to a direct approximation of the issue.

This exploratory study examines and comprehends the relative importance of mobile app attributes from a consumer perspective. Both quantitative and qualitative analysis approaches explore users' behavior and attitudes toward the priorities of mobile app attributes and preferences, identifying correlations between attributes and aggregating individual attributes into groups.

Online convenience sampling and snowball sampling resulted in 417 valid responses. The numerical data are analyzed using the relative to an identified distribution (RIDIT) scoring system and gray relational analysis (GRA), and qualitative responses are investigated using text-mining techniques.

This study finds enhanced nuances of user preferences and provides data-driven insights that might help app developers and marketers create a distinct app that will add value to consumers. The latent semantic analysis indicates relationship structure among the attributes, and text-based cluster analysis determines the subsets of attributes that represent the unique functions of the mobile app.

This study reveals the essential components of mobile apps, paying particular attention to the consumer value component, which boosts user approval and encourages prolonged use. Overall, the results demonstrate that developers must concentrate on its functional, technical and esthetic features to make an app more exciting and practical for potential users.

Most scholarly research on apps has focused on their technological merits, aesthetics and usability from the user's perspective. A post-adoption multi-attribute app analysis using both structured and unstructured data is conducted in this study.

Major public cloud providers, such as AWS, Azure or Google, offer seamless experiences for infrastructure as a service (IaaS), platform as a service (PaaS) and software as a service (SaaS). With the emergence of the public cloud's vast usage, administrators must be able to have a reliable method to provide the seamless experience that a public cloud offers on a smaller scale, such as a private cloud. When a smaller deployment or a private cloud is needed, OpenStack can meet the goals without increasing cost or sacrificing data control.

To demonstrate these enablement goals of resiliency and elasticity in IaaS and PaaS, the authors design a private distributed system cloud platform using OpenStack and its core services of Nova, Swift, Cinder, Neutron, Keystone, Horizon and Glance on a five-node deployment.

Through the demonstration of dynamically adding an IaaS node, pushing the deployment to its physical and logical limits, and eventually crashing the deployment, this paper shows how the PackStack utility facilitates the provisioning of an elastic and resilient OpenStack-based IaaS platform that can be used in production if the deployment is kept within designated boundaries.

The authors adopt the multinode-capable PackStack utility in favor of an all-in-one OpenStack build for a true demonstration of resiliency, elasticity and scalability in a small-scale IaaS. An all-in-one deployment is generally used for proof-of-concept deployments and is not easily scaled in production across multiple nodes. The authors demonstrate that combining PackStack with the multi-node design is suitable for smaller-scale production IaaS and PaaS deployments.

Cruise tourism is the fastest-growing segment of the shipping and port industry. This study aims to develop an analytic model to assess the key criteria and sub-criteria influencing four cruise port's development in Taiwan.

Based on the literature review, four criteria and 13 sub-criteria are developed and analysed by fuzzy analytic hierarchy process (FAHP). Four cruise ports include Kaohsiung, Keelung, Taichung and Hualien ports. The 26 relevant field experts (including cruise operators, governmental officials and academics) were invited to provide information for assessing the sub-criteria in the model.

The results indicate that port infrastructure and facilities are the most important criterion, followed by port-city development plans, port geography and climate and port regulations and services. In addition, the three most important sub-criteria overall are the onshore tourism programme, the city’s historical and cultural features and the green port hinterland transport system. Also, Keelung port is ranked as the best port, followed by Kaohsiung, Taichung and Hualien.

As Asia is an important cruise market in the world (ranked as third) and passenger number in Taiwan has achieved the top two in Asia, denoting Taiwan is a good market to develop an evaluation model of cruise ports. The findings present a holistic picture of the relative importance of the various criteria associated with cruise port development and raise issues related to cruise port marketing and the economic and environmental sustainability of ports and their hinterlands.

This paper aims to build a scientific evaluation index system for regional low-carbon circular economic development. Taking Sichuan Province as the empirical research object, the paper evaluates its low-carbon circular economy (LCCE) development level and proposes policy recommendations for climate change improvement based on the evaluation results.

This paper, first, built an evaluation index system with 30 indicators within six subsystems, namely, economic development, social progress, energy consumption, low-carbon emissions, carbon sink capacity and environmental carrying capacity. Second, develop an “entropy weight-grey correlation” evaluation method. Finally, from a practical point of view, measure the development level of LCCE in Sichuan Province, China, from 2008 to 2018.

It was found that Sichuan LCCE development had a general downward trend from 2008 to 2012 and a steady upward trend from 2012 to 2018; however, the overall level was low. The main factors affecting the LCCE development are lagging energy consumption and environmental carrying capacity subsystem developments.

This paper puts forward relevant suggestions for improving the development of a low-carbon economy and climate change for the reference of policymakers.

This paper built an evaluation index system with 30 indicators for regional low carbon circular economic development. The evaluation method of “entropy weight-grey correlation” is used to measure the development level of regional LCCE in Sichuan Province, China.

This research aims to intuit the trending technology hashtags (artificial intelligence (AI), blockchain, big data, cloud, enterprise resource planning (ERP), information and communication technology (ICT)) in the field of accounting by providing a bibliometric overview of research articles published in 28 years.

A bibliometric analysis of R software was used in this study. The Scopus database was considered to identify the broad research trends related to technology hashtags in the field of accounting from 1984 to 2021, as well as to gain a better understanding of the growing technology research hashtags in that period.

The bibliometric analysis reveals that the trending research topic focused on technology hashtags (AI, blockchain, big data, cloud, ERP and ICT) in the field of accounting has become important after 2010, as references and number of publications found before that year are scarce. The six trending technology hashtags (AI, blockchain, big data, cloud, ERP and ICT) framework for accounting were outlined in this study.

This research examined the trending technology hashtags in the field of accounting. Only scientific papers published in the Scopus database were analyzed.

This research will be able to understand the trending technology hashtags in the field of accounting, as it identifies the most frequent words, cloud (146), big data (91), AI (65), ERP (76), blockchain (6) and ICT (14). It also aids practitioners and scholars in detecting gaps in the existing literature as well as future research trends.

This research result could be a useful resource for researchers, practitioners and accounting professionals who are interested in the latest technology hashtags in the field of accounting. The current research establishes a new framework on the role of trending technology hashtags in accounting research (accounting technology hashtags, framework) comprising of six research pathways (RP) in the four branches of accounting that future researchers can use to think about and construct their study designs in the field of accounting.

This paper aims that privacy research is divided in distinct communities and rarely considered as a singular field, harming its disciplinary identity. The authors collected 119.810 publications and over 3 million references to perform a bibliometric domain analysis as a quantitative approach to uncover the structures within the privacy research field.

The bibliometric domain analysis consists of a combined directed network and topic model of published privacy research. The network contains 83,159 publications and 462,633 internal references. A Latent Dirichlet allocation (LDA) topic model from the same dataset offers an additional lens on structure by classifying each publication on 36 topics with the network data. The combined outcomes of these methods are used to investigate the structural position and topical make-up of the privacy research communities.

The authors identified the research communities as well as categorised their structural positioning. Four communities form the core of privacy research: individual privacy and law, cloud computing, location data and privacy-preserving data publishing. The latter is a macro-community of data mining, anonymity metrics and differential privacy. Surrounding the core are applied communities. Further removed are communities with little influence, most notably the medical communities that make up 14.4% of the network. The topic model shows system design as a potentially latent community. Noteworthy is the absence of a centralised body of knowledge on organisational privacy management.

This is the first in-depth, quantitative mapping study of all privacy research.

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

This review explores the phenomenon of front-of-pack nutrition labels (FoPNLs) in the European Union (EU). FoPNLs highlight the nutritional quality of food and non-alcoholic beverages and help consumers to make healthier choices. The review explores different types of FoPNLs and evaluates their effectiveness.

A policy analysis was conducted, relying on extant academic literature, grey literature and policy documents. The use of current FoPNLs is interpreted in light of national and economic interests.

Our review identifies and describes seven government endorsed FoPNLs that are currently used in the EU. Five are positive endorsement labels (Croatia, Czech Republic, Denmark, Lithuania, Slovenia and Sweden), which only provide a positive indication on more healthy products. The Keyhole is used in three EU countries (Denmark, Lithuania and Sweden), while the others are used in one country each. The Nutri-Score represents a summary label, which provides an overall grade of how healthy a product is. It is used in six countries (Belgium, France, Germany, Netherlands, Spain and Luxembourg). Finally, the Nutrinform battery is a nutrient-specific non-interpretive scheme, indicating the content of nutrients in a portion of a food product. All identified labels are only used on a voluntary basis, encouraging selective use.

This review contributes to a significant discussion about food labeling in the EU. It summarizes existing approaches and evaluates them in terms of their effectiveness. The current schemes in use reflect regional clustering. The most common scheme is the Nutri-Score. This is predominantly found in western EU states. Another major label is the Keyhole, with summary endorsement schemes being prevalent in northern EU states. The least common is Nutrinform, which has some support in southern EU states. The Nutri-score is most effective although economic interests are pushing for the Nutrinform battery in a small number of states. Finally, the review suggests that all existing FoPNLs are voluntary, these labels fail to provide consumers with adequate information about nutrition quality of food products. The EU needs to mobilize support to agree on a single one.

Digital markets are increasingly constructed by an interplay between (non)human market actors, i.e. through algorithms, but, simultaneously, fragmented through platformization. This study aims to explore how interactional dynamics between (non)human market actors co-codify markets through expressive and networked content across social media platforms.

This study applies digital methods as cross-platform analysis to analyze two data sets retrieved from YouTube and Instagram using the keywords “sustainable fashion” and #sustainablefashion, respectively.

The study shows how interactional dynamics between (non)human market actors, co-codify markets across two social media platforms, i.e. YouTube and Instagram. The authors introduce the notion of sticky market webs of connection, illustrating how these dynamics foster cross-platform market codification through relations of exteriority.

Research implications highlight the necessity to account for all involved entities, including digital infrastructure in digital markets and the methodological potential of cross-platform analyses.

Practical implications highlight considerations managers should take into account when designing market communication for digital markets composed of (non)human market actors.

Social implications highlight the possible effects of (non)human market co-codification on markets and consumer culture, and corresponding countermeasures.

This study contributes to an increased understanding of digital market dynamics by illuminating interdependent market co-codification dynamics between (non)human market actors, and how these dynamics (de)territorialize digital market assemblages through relations of exteriority across platforms.