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“Prosumers” (combining “producer” and “consumers”) describes consumers’ ability to openly share their product/service experiences and thereby drive sales and digital marketing. Understanding what motivates active prosumers to engage in electronic word-of-mouth (eWOM) and share or review their hotel experiences online can help organizations empathize with consumers and use their messages to co-create value. Identifying prosumers’ motivators can enable companies to properly target them as resources for review or consumer feedback studies. This paper aims to investigate the influence of motivators (intrinsic and extrinsic), service quality and age on consumers’ eWOM communication behaviors.

A panel of 204 travelers was surveyed regarding their hotel travel experiences, propensity to write online reviews, preferred review-writing platform, motivations for writing reviews and impressions of service quality. To test the hypotheses, a multivariate regression analysis was performed with eWOM as the dependent variable. Differences in eWOM as a function of preferred review platform were also tested using ANOVA, with a multiple comparison analysis that underlines the differences between prosumers who prefer different types of review platforms and their eWOM behaviors.

Both intrinsic and extrinsic motivators, as well as service reliability, had a significant influence on eWOM behavior, while service tangibility had a negative relationship. Additionally, prosumers’ engagement in eWOM about their hospitality experience differed according to their preferred review platform.

The findings of the analysis underline the importance of consumer motivations and of satisfaction with service quality in the context of digital review behavior. For marketing and hospitality research, this shows the benefits of including not only individual characteristics and demographics when analyzing review behavior but also elements such as perceptions of service quality. Given the differences in how the dimensions of service quality affect consumers’ engagement in online review behavior, this represents a very important topic for research and can be included in future studies that analyze the consumer review behavior model.

Regarding the implications for practitioners, this study highlights the important role played by consumer satisfaction with service in the hospitality industry and its effect on their involvement in online reviews. Managers should be focused constantly on offering great service to their guests, while, at the same time, offering them motivations to engage in posting positive reviews about their vacation. Moreover, as the results of this study imply that various dimensions of service quality have a different impact, managers should focus especially on the aspects that consumers consider important and constantly include in their reviews, such as the tangibles dimension. The results of this study also have the potential to provide to businesses more information to improve the social aspects of vacationing, which can not only improve perceptions about service quality but can also have a positive influence on consumers’ motivations.

This paper develops a better understanding of what motivates people to engage in the eWOM communication behavior of writing online hotel reviews, by showing the effect of consumer motivations and service quality variables on prosumers’ engagement in online review behavior.

Increasingly, marketing managers are asked to consider the financial implications, in terms of both book and market values, when making strategic decisions. The purpose of this paper is to investigate the role of marketing expenditures in explaining the variation in the aftermarket performance of a sample of firms conducting initial public offerings (IPOs).

Theories from marketing and finance – market-based assets (MBA) theory and signaling theory respectively – serve as the conceptual basis of this paper. The results of this study, based on a sample of 2,103 IPOs covering the 1996 to 2008 time period, suggest that increased marketing spending positively impacts aftermarket (i.e. stock price) performance.

The authors find that while short-run aftermarket performance is positively and significantly impacted by pre-IPO marketing spending, long-run firm performance measures do not appear to be impacted by pre-IPO marketing spending. Further, pre-IPO marketing spending does not incrementally reduce underpricing or improve long-run performance when the IPO takes place during extreme market conditions such as recessions or hot markets, and these results are important to the shareholders and potential investors in the firm.

Theoretically this paper advances the literature on the marketing-finance interface by extending the MBA and signaling theories. For practice, the results indicate that spending more money on marketing before the IPO and disclosing this information produces positive bottom-line results for the firm.

While Luo (2008) documents a significant relationship between the firms’ pre-IPO marketing spending and IPO underpricing, few studies explore the impact of marketing spending on stock price performance beyond the first day of trading. This paper makes three unique contributions. First, the authors extend Luo’s study by investigating the effect of marketing expenditures on underpricing during extreme market conditions. Second, the authors are the first to examine IPO performance in the long-run as well as the short-run. Finally, the authors assess how long-run performance is impacted by marketing spending during extreme market conditions. The findings of this study has implications for managers and shareholders of firms considering going public through a traditional IPO.

President Bill Clinton has had many opponents and enemies, most of whom come from the political right wing. Clinton supporters contend that these opponents, throughout the Clinton presidency, systematically have sought to undermine this president with the goal of bringing down his presidency and running him out of office; and that they have sought non‐electoral means to remove him from office, including Travelgate, the death of Deputy White House Counsel Vincent Foster, the Filegate controversy, and the Monica Lewinsky matter. This bibliography identifies these and other means by presenting citations about these individuals and organizations that have opposed Clinton. The bibliography is divided into five sections: General; “The conspiracy stream of conspiracy commerce”, a White House‐produced “report” presenting its view of a right‐wing conspiracy against the Clinton presidency; Funding; Conservative organizations; and Publishing/media. Many of the annotations note the links among these key players.

THIS number will appear at the beginning of the Leeds Conference. Although there is no evidence that the attendance will surpass the record attendance registered at the Birmingham Conference, there is every reason to believe that the attendance at Leeds will be very large. The year is one of importance in the history of the city, for it has marked the 300th anniversary of its charter. We hope that some of the festival spirit will survive into the week of the Conference. As a contributor has suggested on another page, we hope that all librarians who attend will do so with the determination to make the Conference one of the friendliest possible character. It has occasionally been pointed out that as the Association grows older it is liable to become more stilted and formal; that institutions and people become standardized and less dynamic. This, if it were true, would be a great pity.

Cleanrooms are highly controlled enclosed rooms where air quality is monitored and ensured to have less contamination according to standard cleanliness level. Air filters are used to optimize indoor air quality and remove air pollutants. Filter media and filtering system are decided as per requirement. Depth filter media are mostly used in cleanroom filtrations. This paper aims to present a comprehensive review of the evolution of cleanroom filter media. It evaluates the advantages and disadvantages of air filter media. It is also studied which air filters have additional properties such as anti-microbial properties, anti-odour properties and chemical absorbent. Development and innovation of air filters and filtration techniques are necessary to improve the performance via the synergistic effect and it can be a possible avenue of future research.

This paper aims to drive the future of air filter research and development in achieving high-performance filtration with high filtration efficiency, low operational cost and high durability. Air pollutants are classified into three types: suspended particles, volatile organic pollutants and microorganisms. Technologies involved in purification are filtration, water washing purification, electrostatic precipitation and anion technology. They purify the air by running it through a filter medium that traps dust, hair, pet fur and debris. As air passes through the filter media, they function as a sieve, capturing particles. The fibres in the filter medium provide a winding path for airflow. There are different types of air filters such as the high-efficiency particulate air filter, fibreglass air filter and ultra-low particulate air filter.

Emerging filtration technologies and filters such as nanofibres, filters with polytetrafluoroethylene membrane are likely to become prevalent over the coming years globally. The introduction of indoor air filtration with thermal comfort can be a possible avenue of future research along with expanding indoor environment monitoring and improving air quality predictions. New air filters and filtration technologies having better performance with low cost and high durability must be developed which can restrict multiple types of pollutants at the same time.

The systematic literature review approach used in this paper highlights the emerging trends and issues in cleanroom filtration in a structured and thematic manner, enabling future work to progress as it will continue to develop and evolve.

The purpose of this paper is to argue that we need to take seriously what affective atmosphere means in public reform. Particular emphasis is put on understanding the mechanisms of hope (Brunsson, 2006) through affective atmosphere (Anderson, 2009) in regards to a management model training course.

Ethnographically, the paper is placed in a Lean coach training course, led by two consultants, in the public care services in a municipality in Sweden. The participants were set to learn the language and techniques of the Lean management model during the course of three training days.

Using affective atmosphere as a theoretical window for how to understand how participants become enthusiastic about public reform, the author puts forward that the enthusiastic, affective atmosphere created in the training room demanded the ingredients of consultants and the mechanism of hope at play. The consultants’ fashioning of the course contributed to the affective atmosphere. But what also triggered the affective atmosphere in the room was the participants’ way of responding, which was much more accidental and founded in the Lean model itself, promising smooth flows and rational organization, and the participants’ ability to keep mechanisms of hope active.

Fotaki et al. (2017) point out that affect has only recently started to be integrated and explored in critical organization studies. Michels and Steyaert (2017) emphasise that affective atmosphere has rarely been used by organization theorists. This is an attempt to contribute to this literature by arguing for the fruitfulness of understanding the mechanisms of hope through affective atmosphere in regards to public management reform. The author also calls attention to the need for ethnographic fieldwork when examining affective atmospheres.

Man has been seeking an ideal existence for a very long time. In this existence, justice, love, and peace are no longer words, but actual experiences. How ever, with the American preemptive invasion and occupation of Afghanistan and Iraq and the subsequent prisoner abuse, such an existence seems to be farther and farther away from reality. The purpose of this work is to stop this dangerous trend by promoting justice, love, and peace through a change of the paradigm that is inconsistent with justice, love, and peace. The strong paradigm that created the strong nation like the U.S. and the strong man like George W. Bush have been the culprit, rather than the contributor, of the above three universal ideals. Thus, rather than justice, love, and peace, the strong paradigm resulted in in justice, hatred, and violence. In order to remove these three and related evils, what the world needs in the beginning of the third millenium is the weak paradigm. Through the acceptance of the latter paradigm, the golden mean or middle paradigm can be formulated, which is a synergy of the weak and the strong paradigm. In order to understand properly the meaning of these paradigms, however, some digression appears necessary.

Chronic diseases are considered as one of the serious concerns and threats to public health across the globe. Diseases such as chronic diabetes mellitus (CDM), cardio vasculardisease (CVD) and chronic kidney disease (CKD) are major chronic diseases responsible for millions of death. Each of these diseases is considered as a risk factor for the other two diseases. Therefore, noteworthy attention is being paid to reduce the risk of these diseases. A gigantic amount of medical data is generated in digital form from smart healthcare appliances in the current era. Although numerous machine learning (ML) algorithms are proposed for the early prediction of chronic diseases, these algorithmic models are neither generalized nor adaptive when the model is imposed on new disease datasets. Hence, these algorithms have to process a huge amount of disease data iteratively until the model converges. This limitation may make it difficult for ML models to fit and produce imprecise results. A single algorithm may not yield accurate results. Nonetheless, an ensemble of classifiers built from multiple models, that works based on a voting principle has been successfully applied to solve many classification tasks. The purpose of this paper is to make early prediction of chronic diseases using hybrid generative regression based deep intelligence network (HGRDIN) model.

In the proposed paper generative regression (GR) model is used in combination with deep neural network (DNN) for the early prediction of chronic disease. The GR model will obtain prior knowledge about the labelled data by analyzing the correlation between features and class labels. Hence, the weight assignment process of DNN is influenced by the relationship between attributes rather than random assignment. The knowledge obtained through these processes is passed as input to the DNN network for further prediction. Since the inference about the input data instances is drawn at the DNN through the GR model, the model is named as hybrid generative regression-based deep intelligence network (HGRDIN).

The credibility of the implemented approach is rigorously validated using various parameters such as accuracy, precision, recall, F score and area under the curve (AUC) score. During the training phase, the proposed algorithm is constantly regularized using the elastic net regularization technique and also hyper-tuned using the various parameters such as momentum and learning rate to minimize the misprediction rate. The experimental results illustrate that the proposed approach predicted the chronic disease with a minimal error by avoiding the possible overfitting and local minima problems. The result obtained with the proposed approach is also compared with the various traditional approaches.

Usually, the diagnostic data are multi-dimension in nature where the performance of the ML algorithm will degrade due to the data overfitting, curse of dimensionality issues. The result obtained through the experiment has achieved an average accuracy of 95%. Hence, analysis can be made further to improve predictive accuracy by overcoming the curse of dimensionality issues.

The proposed ML model can mimic the behavior of the doctor's brain. These algorithms have the capability to replace clinical tasks. The accurate result obtained through the innovative algorithms can free the physician from the mundane care and practices so that the physician can focus more on the complex issues.

Utilizing the proposed predictive model at the decision-making level for the early prediction of the disease is considered as a promising change towards the healthcare sector. The global burden of chronic disease can be reduced at an exceptional level through these approaches.

In the proposed HGRDIN model, the concept of transfer learning approach is used where the knowledge acquired through the GR process is applied on DNN that identified the possible relationship between the dependent and independent feature variables by mapping the chronic data instances to its corresponding target class before it is being passed as input to the DNN network. Hence, the result of the experiments illustrated that the proposed approach obtained superior performance in terms of various validation parameters than the existing conventional techniques.

THE Fifty‐First Conference of the Library Association takes place in the most modern type of British town. Blackpool is a typical growth of the past fifty years or so, rising from the greater value placed upon the recreations of the people in recent decades. It has the name of the pleasure city of the north, a huge caravansary into which the large industrial cities empty themselves at the holiday seasons. But Blackpool is more than that; it is a town with a vibrating local life of its own; it has its intellectual side even if the casual visitor does not always see it as readily as he does the attractions of the front. A week can be spent profitably there even by the mere intellectualist.