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The purpose of this paper is to follow a comparative framework to investigate the effects of outcome quality on loyalty through the mediating role of perceived value in four-star hotels and five-star hotels.

Following a review of the literature, some hypotheses were formulated to examine the effects of outcome quality on attitudinal loyalty and behavioral intention through the mediating role of perceived value. The data guiding the comparative analysis were collected from two groups of visitors staying either in four-start or five-star hotels. The sample included 356 international tourists who stayed overnight in four- or five-star hotels in Kuala Lumpur, Malaysia. Structural equation modeling was used to analyze the data.

Analyzing the data obtained helped to construct three models. In the first model, the effects of outcome quality on attitudinal loyalty and behavioral intention through the mediating role of perceived value in both group of customers was analyzed. In the second model, the effects of outcome quality on behavioral intention through the mediating role of perceived value were compared across the two groups. In the third model, all dimensions of attitudinal loyalty and behavioral intention were combined into one single variable called composite loyalty.

In the first model, the (in)direct effect of outcome quality on both of the dimensions of loyalty (attitudinal loyalty and behavioral intention) was confirmed through perceived value as the mediating variable. The results of processing the second model showed that the impact of outcome quality on behavioral intention was greater in the four-star hotels clients, whereas the effect of perceived value on behavioral intention was greater in the five-star hotels visitors. The third model revealed that the (in)direct effect of outcome quality on composite loyalty through perceived value was greater in the four-star hotels clients than that in the five-star hotels clients.

The purpose of this work is to explore the new possibilities enabled by the recent introduction of RDF-star, an extension that allows for statements about statements within the Resource Description Framework (RDF). Alongside Named Graphs, this approach offers opportunities to leverage a meta-level for data modeling and data applications.

In this extended paper, the authors build onto three modeling use cases published in a previous paper: (1) provide provenance information, (2) maintain backwards compatibility for existing models, and (3) reduce the complexity of a data model. The authors present two scenarios where they implement the use of the meta-level to extend a data model with meta-information.

The authors present three abstract patterns for actively using the meta-level in data modeling. The authors showcase the implementation of the meta-level through two scenarios from our research project: (1) the authors introduce a workflow for triple annotation that uses the meta-level to enable users to comment on individual statements, such as for reporting errors or adding supplementary information. (2) The authors demonstrate how adding meta-information to a data model can accommodate highly specialized data while maintaining the simplicity of the underlying model.

Through the formulation of data modeling patterns with RDF-star and the demonstration of their application in two scenarios, the authors advocate for data modelers to embrace the meta-level.

With RDF-star being a very new extension to RDF, to the best of the authors’ knowledge, they are among the first to relate it to other meta-level approaches and demonstrate its application in real-world scenarios.

This work presents the part of the research in the integration of the remotely piloted aircraft systems (RPAS) in non-segregated airspace. The purpose of this study is to elaborate the reference shape of the Standard Instrument Arrivals (STARs) procedures of controlled airports. The STARs parameters are unique for the aerodromes and depend on navigational aids (NAVAIDs), manoeuvres and aircraft categories. Therefore, the elaboration of reference shapes was advisable in the context of RPAS integration research.

The models were based on the procedure design guidelines by International Civil Aviation Organization. The statistics of existing STARs were prepared using Aeronautical Information Publications to determine the representative procedural parameters. Construction of procedural shapes required to define the nominal flight path and tolerance areas.

In statistics, the standard deviation of distances was below the determined reference mean values, thus the models were convergent with existing procedures.

The modelling was limited to initial, intermediate, final and missed approach segments. Arrival segment was not modelled. NAVAIDs include Instrument Landing System Category 1 (in final and missed approach) and very high-frequency omni-directional ranging or global navigation satellite systems (in initial and intermediate approach segments).

Prepared models may be used in research in the integration of the new types of aerial vehicles in existing air traffic management systems.

The reference STARs possess commonly used procedural manoeuvres (straight-in, turn, racetrack and base turn) and different NAVAIDs. The parameters of approach segments were determined as representative of the existing procedures. Moreover, the models are suitable to place at arbitrary origin and runway axis bearing.

This paper aims to determine the network efficient topology for low power and lossy networks (LLNs) using routing protocol for LLN (RPL) with respect to the increase in network size and propose a novel approach to overcome the shortcomings of the existing models.

The authors have used Contiki OS/Cooja simulator to conduct experiments on primarily four topologies (star, bus/linear, ring/eclipse and random). They have implemented RPL protocol using Sky motes for each topology from 10, 20, 30 and up to 70 nodes. Consequently, after 24 h of experimentation, the readings have been noted and, alongside, a comprehensive comparative analysis has been performed based on the network density and metric parameters: packet delivery ratio (PDR), expected transmission (ETX) and power consumption. Further, a hybrid model is proposed where the additional factors of mobility, multiple sink and a combination of static and mobile nodes are introduced. The proposed model is then compared with the star model (all static nodes and star topology) and the dynamic model (all mobile nodes) to analyze the efficiency and network performance for different network sizes (28, 36, 38 and 44 nodes). The mobility is introduced using BonnMotion tool in Contiki OS.

Simulation results have shown that the star topology is most network efficient when compared with bus/linear, ring/eclipse and random topologies for low density and high scalable network. But when the same setup is compared with the proposed hybrid model, the proposed model shows a significant improvement and gives the best and efficient network performance with highest PDR (average improvement approximately 44.5%) and lowest ETX (average improvement approximately 49.5%) comparatively.

Also, these findings will benefit the deployment of smart devices used in advanced metering infrastructure, road side units and in various industrial applications such as traffic monitoring system, electronic toll collection and traffic analysis in the smart grid infrastructure.

The impact of topology is significant and detailed analysis is required to understand the impact of different topologies of the nodes in the network for the present and the future scenarios. As very few research studies have discussed this gap, this research paper is quintessential and shall open novel future potential direction. Also, the proposed approach of hybrid model with mobility has not been considered in the literature yet.

The purpose of this paper is to expand on the sparse literature on non-linear modelling in South Africa and test for non-linearity of the market cycle on the Johannesburg Stock Exchange, with specific focus on a particular non-linear model – a Smooth Transition Auto-Regressive (STAR) model.

Non-linear estimation methods are used to describe the market cycle, as defined by equity prices, for the period 1998-2010.

In applying the STAR model to daily, weekly and monthly returns data, it was found that the fit of this family of models differs heavily based on the frequency of data used. The daily Logarithmic STAR (LSTAR) model was found to be the best fit relative to other data frequencies. Indeed, the weekly LSTAR model, while still appropriate to use, was less apt at forecasting than its daily counterpart. Monthly return data indicated that a linear AR model was more appropriate than a non-linear one.

The results assist in understanding the cyclical nature of emerging markets as well contributing to the understanding of creating a portfolio consisting of international securities. If one can show that equity returns in a particular emerging market follow non-linear behaviour, expanding this hypothesis to other emerging markets enables a minimum variance portfolio to be constructed that is informed by returns changing over time.

The findings can be used for further research into share price modelling, portfolio management and perhaps as an avenue into the reasoning behind the formation of market cycles.

This paper examines the price setting behaviour over time and space in the Athens residential market. In periods of house price inflation asking prices are often based upon the last observed highest selling price achieved for a similar property in the same micro-location. However, in a falling market, prices may be rigid downwards and less sensitive to the most recent transaction prices, weakening spatial effects. Furthermore, the paper considers whether future price expectations affect price setting behaviour.

The paper employs a dataset of approximately 24,500 property values from 2007 until 2014 in Athens incorporating characteristics and locational variables. The authors begin by estimating a baseline hedonic price model using property characteristics, neighbourhood amenities and location effects. Following this, a spatio-temporal autoregressive (STAR) model is estimated. Running separate models, the authors account for spatial dependence from historic valuations, contemporaneous peer effects and expectations effects.

The initial STAR model shows significant spatial and temporal effects, the former remaining important in a falling market contrasting with previous literature findings. In the second STAR model, whilst past sales effects remain significant although smaller, contemporaneous and price expectations effects are also found to be significant, the latter capturing anchoring and slow adjustment heuristics in price setting behaviour.

As valuations used in the database are based upon comparable sales, then in the recessionary periods covered in the dataset, finding comparables may have become more difficult, and hence this, in turn, may have impacted on valuation accuracy.

In addition to past effects, contemporaneous transactions and expected future values need to be taken in consideration in analysing spatial interactions in housing markets. These factors will influence housing markets in different cities and countries.

The information content of property valuations should more carefully consider the relative importance of different components of asking prices.

This is the first paper to use transactions data over a period of falling house prices in Athens and to consider current and future values in addition to past values in a spatio-temporal context.

This study aims to understand how different levels of streamer channels influence luxury brand sales in live streaming commerce. This study also seeks to understand the conditions under which luxury brands may benefit more from different level streamer channels.

Panel data were collected from 17 international luxury brands on the Douyin live streaming platform in an 18 week period from August to December 2020 and analyzed by using a two-way fixed effects model.

The authors compared different mega-, macro- and micro-streamer channels within live streaming commerce and found that the densities of mega- and macro-streamer channels had significant positive impacts on luxury brand sales in live streaming commerce. Moreover, the effects of the density of streamer channel on luxury brand sales were moderated by such variables as product line breadth, product line depth, product type (star/non-star) and product price (high/low). The authors found that product line breadth and depth could reduce the positive impact of the densities of mega- and macro-streamer channels on luxury brand sales. For star products and high-priced products, the relationship between the density of mega-streamer channel and luxury brand sales was more likely to be observed than for non-star products and low-priced products. The relationship between the density of macro-streamer channel and luxury brand sales was more likely to be observed in low-priced products than in high-priced products.

The findings make important contributions to the literature in that the authors expand the influencer-brand fit theory by proposing a new model of effects of the densities of mega-, macro- and micro-streamer channels on sales performance across different luxury products to improve our understanding of the fit among influencers, brands and products. This helps luxury brands make basic decisions of “who sells” and “sells what” when engaging in live streaming commerce.

The purpose of this paper is to develop an integrated model assessing the frequency and timing between reports on the Association for the Advancement of Sustainability in Higher Education (AASHE) Sustainability Tracking, Assessment and Rating System (STARS) reporting the framework by higher education institutions (HEIs) and the relationship between the STARS score and reputation (enrollment), finances (endowment) and performance (emissions).

The development of the theoretical model is based on learning, signaling and legitimacy theories. This study collects data from the AASHE STARS to indicate the rating level of 202 HEIs, control variables, enrollment, endowments and emissions. The hypotheses were tested using generalized linear models.

Findings suggest that as HEIs report on their sustainability activity, they learn to report better but that there is also an “un-learning” aspect if the HEI skips reporting in a period. The results support the main hypothesis that there is a relationship between reporting and engagement with the HEIs in the form of enrollment and endowments. Finally, the findings provide evidence that the HEIs’ reporting is associated with a reduction in emissions.

The findings suggest that HEIs should develop a reporting strategy on a standardized framework such as AASHE STARs and they ought to codify the approach to learn from prior reporting. Students and alumni are increasingly seeking to engage the HEI in the sustainability process and the report is a mechanism for signaling activities.

The findings suggest that AASHE STARS scores may be used by HEIs as a signaling mechanism to stakeholders of their commitment to sustainability. The signal is a mechanism to reduce information asymmetry between the HEI and stakeholders who may want more information on the institution’s attempts toward sustainability but lack access to information. Further, HEI partners have a mechanism to assess the overall level of commitment of the HEI toward sustainability and can, therefore, engage accordingly.

There has been significant work on signaling theory and sustainability. However, the relationship between STARs reporting as a signal that legitimates the HEI, learning how to report well and HEI performance has received less attention. The current study demonstrates that the STARS framework as a reporting mechanism signals the HEIs’ level of commitment to sustainability thereby legitimating it resulting in improved performance.

The purpose of this paper is to find out whether the election of star analysts leads to the conflict of interests between analysts\institutional investors and individual investors. And then, further investigate how the election results to influence the individual investors’ decision making.

Given the fact that earnings forecasts and stock ratings are the most important foundations for the investor’s investment decision, the authors investigate the relationship among the earnings forecasts, abnormal returns and the election of star analyst. This paper further analyzes the impact factors on investors’ decision. The data used in this paper for star analysts’ information, analysts’ forecast and recommendations, as well as stock performances-related data are from 2005 to 2012.

This paper finds that mass media cannot select analysts with high forecast accuracy, and then misleads investors. It demonstrates that the analysts with poorer forecast ability and more optimistic stock recommendations are more prone to be entitled as star analysts by mass media, and these titled star analysts tend to show a poorer performance. Therefore, the star analyst worsens investors’ cognition on analysts forecast ability and then misleads investors’ decision making.

Media plays a critical role in corporate governance, information collection and diffusion and reducing the information asymmetry, however, it is good to know the role of media in financial markets from a broader perspective. Because media may also bring negative factors to the financial markets such as misguiding the investors and intensify the conflict of interests between analyst and individual investors.

This paper supports a new perspective of the role of mass media in financial market, which is different from existing studies.