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Academic dishonesty is a global challenge, with organizational and economic repercussions of the most undesirable kind. Worldwide, efforts are made to establish external and internal factors that contribute to the spread of unethical behavior, so that based on empirical evidence the most useful approaches to mitigating the phenomenon can be found. The present chapter has two major objectives. A first objective is to explore the steps taken by higher education institutions in Romania regarding the construction of an ethical infrastructure, highlighting the strengths and weaknesses. The author will refer to civil society initiatives and universities’ efforts, including legislative efforts. The second objective is to identify a set of lessons from international research that will support the intervention in the direction of building an academic ethical culture in Romania. Given the level of academic fraud demonstrated through research, the process of building university integrity is extremely difficult in Romania. The intervention of the political factors at the university level contributes to a great instability, sabotaging the process of implementing ethical standards. Ethics infrastructures from Romanian universities are incomplete, focusing on two formal components, namely the ethical code and the ethics commissions. There is no coherent chain of ethical decision, ethical management does not actually work. In conclusion, in Romanian universities, ethical principles are not a top priority, as they clash with organizational and governance practices.

This study aims to develop the first Theory of Technological Response and Progress in Chaos (TRPC) and examine the case of technological development during the COVID-19 pandemic. The research objectives of this study were to: identify the key technologies that act as a response mechanism during the chaos event, specifically in the case of COVID-19; examine how technologies evolve, develop and diffuse in an immediate crisis and a chaotic environment; theorise various types and periods of technological response and progress during the emergence of chaos and the stages that unfold; and develop policy-oriented recommendations and establish technological foundations to address subsequent chaos events.

This study used the grounded theory as a methodology with a mixed-method approach that included quantitative and qualitative methods. The authors used the quantitative method to assist with the qualitative step to build the TRPC theory. Accordingly, this study integrated machine learning and text mining approaches to the qualitative data analysis following the steps of the grounded theory approach.

As a result of the TRPC theory development process, the authors identified three types of technologies (survival, essential and enhancement technologies) and five types of periods (stable, initial, survival-dominant, essential-dominant and enhancement-dominant periods) that are specific to chaos-technology interactions. The policy implications of this study demonstrate that a required technological base and know-how must be established before a chaotic event emerges.

Concerning the limitations of this study, social media data has advantages over other data sources, such as the examination of dynamic areas and analyses of immediate responses to chaos. However, other researchers can examine publications and patent sources to augment the findings concerning scientific approaches and new inventions in relation to COVID-19 and other chaos-specific developments. The authors developed the TRPC theory by studying the COVID-19 pandemic, however, other researchers can utilise it to study other chaos-related conditions, such as chaotic events that are caused by natural disasters. Other scholars can investigate the technological response and progress pattern in other rapidly emerging chaotic events of an uncertain and complex nature to augment these findings.

Following the indications of the OECD (2021a) and considering the study conducted by the European Parliamentary Research Service (Kritikos, 2020), the authors identified the key technologies that are significant for chaos and COVID-19 response using machine learning and text intelligence approach. Accordingly, the authors mapped all technological developments using clustering approaches, and examined the technological progress within the immediate chaos period using social media data.

The key policy implication of this study concerns the need for policymakers to develop policies that will help to establish the required technological base and know-how before chaos emerges. As a result, a rapid response can be implemented to mitigate the chaos and transform it into a competitive advantage. The authors also revealed that this recommendation overlaps with the model of dynamic capabilities in the literature (Teece and Pisano, 2003). Furthermore, this study recommends that nations and organisations establish a technological base that specifically includes technologies that bear 3A characteristics. These are the most crucial technologies for the survival- and essential-dominant stages. Moreover, the results of this study demonstrate that chaos accelerates technological progress through the rapid adoption and diffusion of technologies into different fields. Hence, nations and organisations should regard this rapid progress as an opportunity and establish the prior knowledge base and technologies before chaos emerges.

The authors have contributed to the chaos studies and the relationship between chaos and technological development by establishing the first theoretical foundation using the grounded theory approach, hereafter referred to as the TRPC theory. As part of the TRPC theory, the authors present three periods of technological response in the following sequence: survival technology, essential technology and enhancement technology. Moreover, this study illustrates the evolving technological importance and priorities as the periods of technological progress proceed under rapidly developing chaos.

The purpose of this paper is to discover the common and differentiating food factors that affect tourists’ gastronomy satisfaction by comparing tourists’ gastronomic experiences at two culinary destinations in the Mediterranean region.

A total of 396 usable questionnaires were collected using a convenience sampling method from Cordoba (Spain) and Hatay (Turkey). The data were analysed through descriptive and multivariate analysis methods, including frequency analysis, multiple independent samples t-tests, χ² analysis and multiple regression analyses.

The results indicate that both of the destinations primarily attract existential type gastronomic tourists; however, they also attract recreational and diversionary types of tourist, particularly in Cordoba. From the perspective of Mediterranean cuisine, food quality and traditional gastronomy were determined to be common crucial factors for tourists’ food consumption satisfaction, which outweighs the effects of price, facilities and atmosphere. In addition, service and hospitality and innovation and new tastes in the dishes are the significant factors; however, factors vary according to the destination.

This study makes a significant contribution to the field food tourism by identifying common significant and insignificant and differentiating food factors that affect tourists’ gastronomic satisfaction in culinary destinations within the same geographical region. The results have the potential to provide a broader perspective for destination marketers and culinary establishments.

This study aims to examine cash dividend practices of travel and leisure (T&L) companies listed on the London Stock Exchange (LSE).

The study uses a panel data set of 524 firm-year observations of 55 unique publicly listed UK T&L companies between 2007 and 2019. First, it uses a modified version of Lintner’s (1956) partial adjustment model for analysis regarding the target payout ratio and dividend smoothing. Second, it performs logit and Tobit models in ascertaining the association between financial characteristics and divided decisions of T&L firms. Finally, it applies the modified specification of the partial adjustment model on different sub-samples that are partitioned based on various financial factors to determine how the financial characteristics of T&L companies affect their dividend behavior.

The results show that UK T&L companies have long-term payout ratios and adjust their cash dividends by moving gradually to their target at a serious degree of smoothing. The findings also detect that financial characteristics of T&L firms (i.e. profitability, debt and size) have significant effects on their dividend payments decisions. In particular, more profitable and larger T&L corporations are more likely to pay cash dividends, whereas T&L companies with more debt are less likely to pay cash dividends in the UK. The results further reveal that although such financial characteristics also have important impacts on the target payout ratios and dividend smoothing levels, UK T&L companies generally adopt stable dividend policies over the period 2007-2019.

This is thought to be the first study to provide insights on dividend policy practices of UK travel and leisure corporation listed on the LSE.

Blockchain is expected to have a significant impact on Systems of Innovation as the new General Purpose Technology. The purpose of this study is to investigate how Blockchain can revolutionise the Systems of Innovation by investigating its overall structure, actors and relationships.

This study used the systematic mapping method to explore and integrate the Blockchain and Systems of Innovation literature for the creation of a new conceptual model of Blockchain-enabled Systems of Innovation. In that scope, 37 Blockchain-related and 32 Systems of Innovation-related papers, besides two major books in the field of Blockchain, have been reviewed and then integrated based on the Systems Thinking approach.

The key findings for Blockchain-enabled Systems of Innovation are that there is (1) an increased distribution of networks and collaborations, (2) increased trust through the use of reputation systems, (3) an emerging new nature of platform characteristics, (4) a democratisation of entrepreneurship by the new funding landscape and (5) an increased significance of technological drivers, such as energy.

The study shows new Systems of Innovation-related research implications. Accordingly, a new type of actor, relationship and attribute has been introduced where the boundaries of the role definitions are blurred and more distributed. This is where larger organisations can expect to lose their central position. The different types of actors are replaced by a network of actors as a result of the distributed new Blockchain-based system. The threshold for the Bottom of the Pyramid is expected to be reduced, leading to a more democratised innovation system.

Blockchain appears to reduce the effects of distrust in collaborative innovation practices with its consensus mechanisms and the new Blockchain-enabled Systems of Innovation is expected to revolutionise the interactions in the future.

There are very few studies that have been found to integrate innovation management practices with Blockchain. This is the first Blockchain-based Systems of Innovation study enabling the fundamental revision of its structure, types of relationships and actors.

The study aimed to establish football fans' levels of social identity, team loyalty, and behavioral intentions toward sports consumption and to determine the relationship between these variables using a structural equation model.

Research methods: The research model was tested by data analysis using AMOS 23.0. The study sample included 518 football fans with mean age 30.87 (±10.15) years. Data were collected using an online questionnaire delivered to football fans via social media.

The results supported the study hypotheses and showed that social identity and team loyalty had a significant effect on behavioral intention toward sport consumption for football teams. Social identity was found to have a direct effect on sport consumption behaviors. It was further established that team loyalty was a mediator in the relationship between the fans' social identity and behavioral intention toward sports consumption.

The study planned to collect the data at the stadium entrance before the game; however, it was not possible to reach the fans face-to-face due to the COVID-19 pandemic, and therefore, the data were collected online.

By understanding how the extent of social identity affects sports consumption intention, sports club managers can achieve a higher level of fan loyalty to their teams.

The present study provides additional insight into the available literature on team identification. It demonstrated that team identification was associated with social identity and that social identity had an impact on sports consumption behaviors.

This study is an original study in that it is a study in which the social identity scale is used together with the variables of team loyalty and sports consumption behavior intention. The findings of this study help understand the processes that shape the behavioral intentions of football fans toward sports consumption. Furthermore, the mediating role of the developed model was tested using team loyalty.

This paper aims to examine dynamic equicorrelations (DECO) and directional volatility spillover effects among four energy futures markets, namely, West Texas Intermediate crude oil, heating oil, natural gas and reformulated blendstock for oxygenate blending gasoline, by using a multivariate fractionally integrated asymmetric power ARCH–DECO–generalized autoregressive conditional heteroskedasticity (GARCH) model and the spillover index technique.

The empirical analysis uses the dynamic equicorrelation model of Engle and Kelly (2012) to examine time-varying correlations at equilibrium. The authors further analyze dynamic volatility transmission among energy futures by using Diebold and Yilmaz (2012) dynamic spillover index based on generalized value-at-risk framework.

The empirical results provide evidence of heightened equicorrelations at times of financial turmoil. More specifically, the dynamic spillover analysis shows that volatility is transmitted predominantly from crude oil to the other markets and risk transfer among four markets exhibits asymmetries. Spillovers are found to be highly responsive to dramatic events such as the 9/11 terror attack, 2008–2009 global financial crisis and 2014–2016 oil glut.

The results of this study have important practical implications for investors, portfolio managers and energy policymakers as the presence of time-varying co-movements and spillovers suggests the need for dynamic trading strategies. There are also implications regarding risk management practices, as there is evidence of increased volatility transmission at times of financial turmoil and uncertainty. Finally, the results provide insights to policymakers in a better understanding of the spillover dynamics.

This paper investigates the DECOs and spillover effects among crude oil, natural gas, heating oil and gasoline futures markets. To the best of the knowledge, this is one of a few studies that examine co-movements and risk transfer in energy futures in a comprehensive framework.

Various measures exist to determine the quality, however defined, of records in a database or catalogue but there has been less work in developing quality measures for catalogues or databases. UKOLN has carried out work on record quality and catalogue/database performance measurement since 1980. Recently UKOLN has extended its work on the currency of records in the BNB files on the British Library database to a number of other sources of bibliographic records. The results of the Multi‐Source Currency survey presented here have highlighted a number of issues relating to the bibliographic record coverage of the UK imprint.

The purpose of this paper is to investigate the influence of time steps, integration methods, and saturation modeling on the accuracy of the synchronous machine model. This model is compared with the PSCAD built‐in synchronous machine model in order to compare the accuracy of one of the most used synchronous machine models in a commercially available software versus a well‐documented and widely accepted state‐space synchronous machine model.

In the paper, a synchronous condenser with the saturation phenomenon is modeled using state‐space equations in the rotating dq‐reference frame and is implemented both in Matlab/Simulink and PSCAD. Integration methods of up to the fifth order are implemented for increased accuracy. The saturation modeling includes modeling of the saturation in both d‐ and q‐axis. A steady‐state and dynamic performance comparison towards the built‐in PSCAD synchronous machine model is performed. The saturation modeling does not include the saturation of the leakage fluxes.

When the forward Euler method is used, in order to obtain less than 5 percent error, the time step should not exceed 5 μs. The third‐order Runge‐Kutta method is the preferred choice and it provides desired accuracy when the time step is equal or smaller than 1,000 μs. The built‐in PSCAD model satisfies the error criteria for time steps smaller than 300 μs. A small discrepancy of 2 percent is found during the steady‐state test.

The paper presents the performance of the higher order integration methods in an EMTP‐type software environment where the trapezoidal integration method is most often used. It provides a good guide for building an owner‐defined model. A comparison of a dynamic performance between the publicly documented state‐space and a synchronous machine models commonly used for power system transient studies is presented.