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Model based clustering of political finance regimes: developing the regulation of political finance indicatorPolitical finance literature lacks a common framework for classifying regulatory systems. As these tools are influential in the identification of generalizable relationships, studies assessing political finance in areas such as corruption, competition, and electoral outcomes, often present case specific findings. Using updated International IDEA data, the application of a Multiple Correspondence Analysis and Model Based Clustering framework presents a variable to measure levels of regulation; the ‘Unregulated’, ‘Partially Regulated’ and ‘Strongly Regulated’ system types; and statistics for assessing the certainty of each country’s classification. Applying this methodology to a 180-country sample represents an improvement on previous studies which, due to data limitations, have often used reductive methods and limited sampling. In closing, the ‘Regulation of Political Finance Indicator’ is introduced via Multinomial Logistic Regression, where analyses from prior literature are revisited. Avenues for further study are provided, which may seek to identify generalizable relationships in the areas described above, while also looking to produce ongoing panel data.
Satisfaction, destination quality and behavioral intentions: the case of UNESCO Heritage Sites in JordanThis study investigated the effect of quality of the cultural heritage destination on the Behavioural Intentions of its visitors and the role that Satisfaction plays in determining their Behavioural Intentions. The study questioned the ability of Destination Quality to predict the tourists’ Behavioural Intentions by using this explanatory variable as the sole determinant of tourist’s Satisfaction. By so doing, this study proposes alternative predictors of Satisfaction and, consequently, behaviour. The lack of a dedicated tool to measure quality, Satisfaction and Behavioural Intention in cultural heritage sites called for designing a new, dedicated instrument, called the Destination Quality Scale, and, hereafter, referred to as DESTINQUAL Scale, to achieve the study goals. Although this study adopted a mixed‐method approach, it is mainly quantitative in nature. The interviews helped in development of the questionnaire whereas the data needed for hypothesis testing and analysis were quantitative data that were processed following quantitative analysis methods. Of the 500 questionnaire forms distributed to local and foreign tourists in five World Cultural Heritage sites in Jordan, exactly 447 forms were retrieved. Of these, only 388 forms were usable. The research data were analysed using the Statistical Package for the Social Sciences (SPSS) and Smart PLS software. The results of hypothesis testing showed that all studied measures of quality of the cultural heritage site positively affected tourist’s Satisfaction and Behavioural Intentions to varying degrees. In addition, this study found that the individual effect of each of the investigated Destination Quality measures on the tourist’s Behavioural Intention were partially mediated by Satisfaction, except for Authenticity, whose effect on the tourist’s Behavioural Intention was fully mediated by Satisfaction. These findings confirm the proposition of this study that visitors of cultural heritage sites have special interests in these destinations and look for experience beyond the service quality. This study contributes to the theoretical knowledge by uncovering the factors that lead to certain behaviours in the cultural visitation context, which is a contribution that was only possible after development of a dedicated scale (the DESTINQUAL Scale) to assess quality of the cultural heritage destinations, which is, actually, a scale that can be used in any cultural heritage site in the World. At the methodological level, the study challenged the diagnostic ability of the widely used Expectancy‐Disconfirmation Theory in the cultural visitation context by providing evidence on that Satisfaction can be assessed via other measures than the service quality measures. At the practical level, the research tool and study results can help tourism planners and decision makers in heightening the level of Satisfaction of the cultural heritage site visitors and warranting the desired behavioural intentions, ultimately to contribute to enhancement of tourist’s experience and to tourism sector in Jordan.
Energy efficient technique for Hadoop MapReduce cluster managementBig data analytics, with datasets of terabyte and petabyte size, is now a reality for businesses. A widely used solution for data centres is the MapReduce model on open‐source Hadoop. Many organisations processing real‐time data of this magnitude rely on the Hadoop MapReduce model, and the massive increase in data generation means that even small to medium enterprises (SMEs) have a requirement for big data analysis. The business insights gained from this real‐time data analysis are vital in the modern world, and although this can be outsourced to data centres, SMEs will be more sustainable if they can do this for themselves. However, the increase in the amount of data has resulted in a corresponding increase in the amount of energy used for processing. The need to minimise the use of energy, both in terms of cost and ecology, is the main rationale behind this research, and energy‐efficiency will be the key to sustainability in the twenty‐first century. The initial categorisation of energy‐efficient methods for Hadoop components has been the starting point for a comparative evaluation in this research. The research has used Hadoop MapReduce performance modelling in a series of mathematical analyses and experimental tests, and these have led to the identification and design of an energy‐efficient model. This proposed model uses a novel method of data partitioning using virtual chunks. The idea is that rather than accessing the entire data file, blocks, or chunks of data are accessed that are virtually linked. The accuracy and efficiency of the proposed design have been evaluated mathematically and the results presented graphically, and the method has been shown to minimise the processing time and complete the different data operations. This reduction of processing time has resulted in minimising the I/O bottleneck of workload applications, thus reducing the amount of energy needed for processing big data. This improved energyefficiency can be maintained for datasets of all sizes and in multiple applications. The results of this research are transferrable and can be used by SMEs of any kind in any area of business.
Financing entrepreneurial innovation within EnglandEntrepreneurship has come to serve as a platform for innovation with majority of the world’s economies seeking to promote structures, policies and environments that encourage it. For entrepreneurial firms to succeed, availability of finance has been identified as a key factor; however, despite availability of new financial tools, a demand‐side gap still exists. This is quite important as entrepreneurial firms have particularly high hurdles to cross when seeking finance due to the complexity and uncertainty associated with their products/services. Several studies have investigated the access to finance problem without yielding unique results, this study adopts a novel conceptual framework where the intellectual capital of firm and its social media capabilities are examined to investigate the empirical relationship that exists between intellectual capital and access to finance. Entrepreneurial orientation and collaboration act as mediators with environmental munificence acting as the moderator. Theoretical triangulation is applied to this study as two main theories namely the Agency theory which provides a framework to assess the agency problems which are information asymmetry and moral hazard within an entrepreneurial setting. The second theory is the resource dependency theory which highlights dependencies created between firms and financiers when external finance is raised. This research adopts a quantitative methodology using questionnaire instrument to collect 488 valid responses. The responses were collected using both in‐person and online channels. The researcher had to engage in entrepreneurial activities to build their social capital by organising global events online, hosting online fireside chats, volunteering on various entrepreneurship mentoring platforms (Posters can be found in the appendix). Data collected was analysed using covariance based structural equation modelling. A detailed and rigorous analysis of data collected is carried out to test the measurement and structural model. Findings include but are not limited to the following: There is a direct and positive relationship between social capital and access to finance due to its legitimising qualities, social media is found to promote collaboration in the firm and with competitors. Structural capital is seen to have a positive relationship with access to finance as robust processes within a firm enables it mitigate information asymmetry problems. Firms possessing entrepreneurial orientation and collaboration capabilities are seen to leverage on their intellectual capital more efficiently thus aiding them to access finance. There is also a positive and direct relationship between environmental munificence and access to finance whereby firms operating in resource rich environments tend to raise external finance more easily. Several contributions are made to academia, practice and policy making that highlight the paramount role of intellectual capital which has become more pronounced as the world moves towards service‐based economies.