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It is generally recognised that companies spend approximately 50% of their marketing budget on promotional activities. Advertising belongs to the most visible areas of a company’s activity. Therefore, it should not be surprising that the average recipient associates marketing with advertising, competitions and leaflets about new promotions delivered to houses or offices. Advertising, especially Internet advertising, is one of the most effective forms of marketing and one of the fastest developing areas of business. New channels of communication are emerging all the time – the Internet, digital television, mobile telephony; accompanied by new forms, such as the so-called ambient media. Advertising benefits from the achievements of many fields of science, that is, psychology, sociology, statistics, medicine and economics. At the same time, it combines science and the arts – it requires both knowledge and intuition. Contemporary advertising has different forms and areas of activity; yet it is always closely linked with the operations of a company – it is a form of marketing communication.

The indices of marketing communication presented in this chapter are generally known and used not only by advertising agencies but also by the marketing departments of many organisations. Brand awareness, advertising scope and frequency, the penetration index or the response rate belong to the most widely used indices; others, like the conversion rate or the affinity index, will get increasingly more significant along with the process of professionalisation of the environment of marketing specialists in Poland and with increased pressure on measuring marketing activities. Marketing indices are used for not only planning activities, but also their evaluation; some of them, such as telemarketing, mailing and coupons, provide an extensive array of possibilities of performance evaluation.

To identify women's sources of information about cervical cancer screening, information which women report receiving during Pap consultations, information they would like to receive, and the relationships between perceived information needs, personal characteristics and information sources.

Logistic regression analysis of questionnaire data obtained from 408 screen‐eligible women resident in east central UK.

Programme documentation and the Pap consultation represent the main sources of information, although a sizeable proportion rely on other sources (e.g. mass media). The range and frequency of information services which women report receiving during their Pap consultations are variable, and around one‐sixth of women report never receiving information. “Always wanting information” is predictable from subject characteristics, which do not map precisely, owing to the variation in frequency of information being supplied. Age and women's main sources of information are significant predictors of perceived information shortfall, and such shortfalls are associated with dissatisfaction with the screening programme.

Covers all aspects of women's attitudes towards satisfactory or unsatisfactory availability of external information in the matter of screening for cervical cancer in the UK.

This research studies private information from extreme price movements or jumps. The authors calculate the private information using a reduced form model from the stochastic volatility jump process and use several statistical robustness tests as well as several frequencies to improve our consistency. This study reveals that private information is significant in explain the existence of jumps in capital markets in Southeast Asia, whereas macroeconomic events cannot explain them. The authors determine empirically that private information in Malaysia, Singapore, Thailand, and Indonesia are not persistent and its value gradually decreases when we use the lower frequency. Based on the Fama–Macbeth regression, this study shows that private information in the capital market has a strong positive relationship with individual returns in Indonesia’s capital market and Thailand’s capital market for all frequencies.

The purpose of this paper is to investigate the impact of the frequency of information acquisition on the frequency of stock trading. The authors also examined if the Big Five personality traits of investor influence the association between information acquisition and stock trading behavior.

The authors adopted NEO Five-Factor Inventory (Costa and McCrae, 1989) inventory to measure the Big Five personality traits of investors and examined the data collected from 541 individual investors of the Chinese stock market. To overcome the potential endogeneity bias, the authors followed two-stage least square method for estimating endogenous covariate by employing instrumental variable analysis. The authors performed probit regression to evaluate the moderating influence of investor personality traits on the association between information acquisition and stock trading behavior. The authors also performed several other tests to check the robustness of the key findings.

This research confirmed the previous findings that the more frequently investors acquire information, the more often they trade in stocks. Moreover, the authors added to the existing literature by providing empirical evidence that the Big Five personality traits moderate the relationship of information acquisition with stock trading behavior. Information acquisition tends to increase stock trading frequency in investors with conscientiousness, extraversion and agreeableness traits. On the other hand, it also has the tendency to decrease the intensity of stock trading in investors with openness and neuroticism traits.

The theoretical model in this study seeks to explain that the psychological factor, namely, investor personality, influences the way an investor interprets signals from information which in turn influences the investor decision to trade in securities. This research suggests that psychological characteristics of investors can be of relevance for policy makers in their attempts to improve their business in the financial services industry.

This study combines both information search literature and behavioral finance literature to investigate whether or not the information acquisition that relates to investors’ asset allocation decisions is influenced by investor personality. The study offers new theoretical insights into investors’ behavior due to the characteristics of the Chinese stock market which are uniquely different from other stock markets in the world. No previous study has been conducted so far in the Chinese stock market to explore variations in the impact of investors’ information acquisition on their stock trading by the Big Five personality and this paper strives to fill this research gap.

The purpose of this paper is to study the fault diagnosis of internal combustion (IC) engine gearbox using vibration signals with signal processing and machine learning (ML) techniques.

Vibration signals from the gearbox are acquired for healthy and induced faulty conditions of the gear. In this study, 50% tooth fault and 100% tooth fault are chosen as gear faults in the driver gear. The acquired signals are processed and analyzed using signal processing and ML techniques.

The obtained results show that variation in the amplitude of the crankshaft rotational frequency (CRF) and gear mesh frequency (GMF) for different conditions of the gearbox with various load conditions. ML techniques were also employed in developing the fault diagnosis system using statistical features. J48 decision tree provides better classification accuracy about 85.1852% in identifying gearbox conditions.

The proposed approach can be used effectively for fault diagnosis of IC engine gearbox. Spectrum and continuous wavelet transform (CWT) provide better information about gear fault conditions using time–frequency characteristics.

In this paper, experiments are conducted on real-time running condition of IC engine gearbox while considering combustion. Eddy current dynamometer is attached to output shaft of the engine for applying load. Spectrum, cepstrum, short-time Fourier transform (STFT) and wavelet analysis are performed. Spectrum, cepstrum and CWT provide better information about gear fault conditions using time–frequency characteristics. ML techniques were used in analyzing classification accuracy of the experimental data to detect the gearbox conditions using various classifiers. Hence, these techniques can be used for detection of faults in the IC engine gearbox and other reciprocating/rotating machineries.

Science of systems requires a specific and constructive mathematical model and language, which describe jointly such systemic categories as adaptation, self‐organization, complexity, evolution, and bring the applied tools for building a system model for each specific object of a diverse nature. This formalism should be connected directly with a world of information and computer applications of systemic model, developed for a particular object. The considered information systems theory (IST) is aimed at building a bridge between the mathematical systemic formalism and information technologies to develop a constructive systemic model of revealing information regularities and specific information code for each object.

To fulfill this goal and the considered systems' definition, the IST joins two main concepts: unified information description of interacted flows, initiated by the sources of different nature, with common information language and systems modeling methodology, applied to distinct interdisciplinary objects; general system's information formalism for building the model, which allows expressing mathematically the system's regularities and main systemic mechanisms.

The formalism of informational macrodynamics (IMD), based of the minimax variational principle, reveals the system model's main layers: microlevel stochastics, macrolevel dynamics, hierarchical dynamic network (IN) of information structures, its minimal logic, and optimal code of communication language, generated by the IN hierarchy, dynamics, and geometry. The system's complex dynamics originate information geometry and evolution with the functional information mechanisms of ordering, cooperation, mutation, stability, diversity, adaptation, self‐organization, and the double helix's genetic code.

The developed IMD's theoretical computer‐based methodology and the software has been applied to such areas as technology, communications, computer science, intelligent processes, biology, economy, management, and other nonphysical and physical subjects.

The IMD's macrodynamics of uncertainties connect randomness and regularities, stochastic and determinism, reversibility and irreversibility, symmetry and asymmetry, stability and instability, structurization and stochastization, order and disorder, as a result of micro‐macrolevel's interactions for an open system, when the external environment can change the model's structure.

This paper aims to investigate the impact of sources of information on trading behavior by analyzing the influence of investor personality in Chinese futures market.

The authors adopted the Big Five personality framework and examined the survey results of individual investors (n = 333) in Chinese futures market. Personality traits of futures investors were measured by the NEO-Five Factor Inventory (Costa and McCrae, 1989) which is a shortened version of revised NEO personality inventory of the Big Five model (Costa and McCrae, 1992). Confirmatory factor analysis was conducted to assess the fitness of model. Structural equation modeling was used to evaluate the moderating influence of investor personality traits on the association between source of information and trading behavior.

The results confirm the previous findings that the sources of information used by investors as a foundation of their financial choices have a significant impact on trading frequency. The authors also provide an empirical evidence that investor personality traits moderate the relationship between sources of information and trading behavior. Financial advice from professionals is likely to increase trading frequency in investors with neuroticism and openness personality traits, and to reduce trading frequency in conscientious and extravert investors. Similarly, financial information acquired via word-of-mouth communication results in more trading in extravert and agreeable investors. Finally, information acquisition from specialized press causes more adjustment of conscientious investors’ portfolios. Theoretical explanations, implications and recommendations for future research are discussed.

This study combines information search and behavioral finance literature to demonstrate that the impact of various sources of market information on asset allocation decisions is influenced by investor personality. No previous study has been conducted yet to explain variations in the impact of sources of information on trading behavior by the Big Five personality traits and this paper seeks to fill this gap in Chinese futures market.

Citations have been used as a common basis to measure the academic accomplishments of scientific books. However, traditional citation analysis ignored content mining and without consideration of citation equivalence, which may lead to the decline of evaluation reliability. Hence, this paper aims to integrate multi-level citation information to conduct multi-dimensional analysis.

In this paper, books’ academic impacts were measured by integrating multi-level citation resources, including books’ citation frequencies and citation-related contents. Specifically, firstly, books’ citation frequencies were counted as the frequency-level metric. Secondly, content-level metrics were detected from multi-dimensional citation contents based on finer-grained mining, including topic extraction on the metadata and citation classification on the citation contexts. Finally, differential metric weighting methods were compared with integrate the multi-level metrics and computing books’ academic impacts.

The experimental results indicate that the integration of multiple citation resources is necessary, as it can significantly improve the comprehensiveness of the evaluation results. Meanwhile, compared with the type differences of books, disciplinary differences need more attention when evaluating the academic impacts of books.

Academic impact assessment of books via integrating multi-level citation information can provide more detailed evaluation information and cover shortcomings of methods based on single citation data. Moreover, the method proposed in this paper is publication independent, which can be used to measure other publications besides books.

The development of models for variables sampled at different frequencies has attracted substantial interest in the recent literature. In this article, we discuss classical and Bayesian methods of estimating mixed-frequency VARs, and use them for forecasting and structural analysis. We also compare mixed-frequency VARs with other approaches to handling mixed-frequency data.

This study examined subjective numeracy and its relationship with accounting judgements on probability issues.

A subjective numeracy scale (SNS) questionnaire was distributed to 231 accounting students to measure self-evaluated numeracy. Modified Bayesian reasoning tasks were applied in an accounting-related probability estimation, manipulating presentation formats.

The study revealed a positive relationship between self-evaluated numeracy and performance in accounting probability estimation. The findings suggest that switching the format of probability expressions from percentages to frequencies can improve the performance of participants with low self-evaluated numeracy.

Adding objective numeracy measurements could enhance results. Future numeracy research could add objective numeracy items and assess whether this influences participants' self-perceived numeracy. Based on this sample population of accounting students, the findings may not apply to large populations of accounting-information users.

Investors' ability to exercise sound judgement depends on the accuracy of their probability estimations. Manipulating the format of probability expressions can improve probability estimation performance in investors with low self-evaluated numeracy.

This study identified a significant performance gap among participants in performing accounting probability estimations: those with high self-evaluated numeracy performed better than those with low self-evaluated numeracy. The authors also explored a method other than additional training to improve participants' performance on probability estimation tasks and discovered that frequency formats enhanced the performance of participants with low self-evaluated numeracy.