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This paper aims to discuss a comprehensive analysis of the effects of torque and flux hysteresis bands on the inverter average switching frequency considering an induction machine drive under the control of the Takahashi DTC strategy.

The analysis of the effects of torque and flux hysteresis bands on the inverter average switching frequency is carried out taking into account the speed range and the sampling period.

It has been found that the inverter average switching frequency could be more or less taken down according to the speed range and the sampling period by selecting suitable flux and torque hysteresis bands.

This work should be extended by an experimental validation of the established results.

The reduction of the inverter switching frequency is of great importance in direct torque controlled induction motor drive as far as it leads to a decrease of the torque ripple and an increase of the efficiency.

For given torque and flux hysteresis bands, the inverter average switching frequency presents nonlinear shape. Given the fact that the flux switching frequency is a linear function of the speed, one can conclude that the nonlinearity of the inverter average switching frequency is due to the torque switching frequency. This statement has been proven by the introduction of the so‐called focal speeds for the torque switching frequency turns to be null.

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.

Over the last 30 years a range of empirical generalisations has been developed about the performance of competitive brands in frequently purchased product categories. These generalisations have been based mainly on European and US data, and this paper addresses the question of whether they also hold in Australia and New Zealand. We examined consumer panel data from four different markets (supermarkets, department stores and retail fuel in Australia and retail fuel in New Zealand) and found similar patterns to those in Europe and the USA, although there were some minor exceptions, and also some interesting variations between markets. Our results suggest that there is much that Australasian marketers can learn from using models such as the Dirichlet, which was developed in the Northern hemisphere, to identify norms and exceptions in their own markets.

The purpose of this paper is to initially use a stochastic model to fit data of customer behavior stickiness and customer value, then estimate the corresponding parameters and use Bayesian rule to calculate its mathematical expectation.

The authors use expectations of customer behavior stickiness as an independent variable, expectations of customer value as a dependent variable, motivations of consumption as moderator and conduct regression analysis to research the relationship among the three. First, we will use the data of behavior for customer network shopping in the questionnaire to establish the random probability model and forecast. Second, we calculate the expected customer behavior stickiness and customer value. Finally, we use resurvey data of 100 subjects after three months (selected randomly from 373 objects) to test the model prediction.

The findings show that customer behavior stickiness has a significant effect on customer value, and the moderating effect of the hedonic motivation of consumption on the relationship above is proved. The value of customers who hold high hedonic motivation of consumption is mainly driven from website’s single visit time, whereas the value of customers who hold low or middle hedonic motivation of consumption is mainly driven from a website’s visit frequency.

The paper proves and quantifies the effects of the customer behavior stickiness for customer value in times of behavior. The results prove the moderation role of consumer motivation of the customer for the path of customer behavior stickiness→customer value, and make clear that the hedonic motivation is a necessary condition of average site visit time that has a significant impact on customer value.

In this project, we propose and test a new device – wearable sociometric badges containing small microphones – as a low-cost and relatively unobtrusive tool for measuring stress response to group processes. Specifically, we investigate whether voice pitch, measured using the microphone of the sociometric badge, is associated with physiological stress response to group processes.

We collect data in a laboratory setting using participants engaged in two types of small-group interactions: a social interaction and a problem-solving task. We examine the association between voice pitch (measured by fundamental frequency of the participant’s speech) and physiological stress response (measured using salivary cortisol) in these two types of small-group interactions.

We find that in the social task, participants who exhibit a stress response have a statistically significant greater deviation in voice pitch (from their overall average voice pitch) than those who do not exhibit a stress response. In the problem-solving task, participants who exhibit a stress response also have a greater deviation in voice pitch than those who do not exhibit a stress response, however, in this case, the results are only marginally significant. In both tasks, among participants who exhibited a stress response, we find a statistically significant correlation between physiological stress response and deviation in voice pitch.

We conclude that wearable microphones have the potential to serve as cheap and unobtrusive tools for measuring stress response to group processes.

Although blood banks have recently started to recruit blood donors through social media platforms, including WeChat, to increase recruitment effectiveness, few researchers have studied their effects on blood donation behavior. The aim of this study is to examine the influence of using official WeChat accounts on repeat blood donation behavior.

This paper used the backstage operation data of official WeChat accounts and blood supply chain management system data from the blood bank for the study to analyze the changes in repeat blood donation behavior. First, to analyze the changes in the average frequency of blood donation per year, average volume of single blood donation and blood eligible rate of repeat blood donors before and after following the official WeChat accounts by difference-in-differences model combined with propensity score matching (PSM-DID). Second, we examined the impact of official WeChat accounts on the proportion of repeat blood donors through survival analysis.

The results show that following WeChat accounts increases the average frequency of blood donation and blood eligible rate of repeat blood donors by 14.36% and 1.19%, respectively, and have no significant effect on the average volume of single blood donation. Further, WeChat accounts have a more significant impact on the average frequency of blood donations per year for workers, farmers, medical staff and groups with education levels of junior high school. In addition, official WeChat accounts can effectively increase the proportion of repeat donors.

The results provide a quantitative basis for the influence of official WeChat accounts on repeat blood donation behaviors. On the one hand, it is of great significance to guide the publicity and recruitment of unpaid blood banks. On the other hand, it provides an evidence for the promotion of official WeChat accounts.

Despite the growing availability of scanner-panel data, surveys remain the most common and inexpensive method of gathering marketing metrics. The purpose of this paper is to explore the size, direction and correction of response errors in retrospective reports of category buying.

Self-reported purchase frequency data were validated using British household panel records and the negative binomial distribution (NBD) in six packaged goods categories. The log likelihood theory and the fit of the NBD model were used to test an approach to adjusting the errors post-data collection.

The authors found variations in systematic response errors according to buyer type. Specifically, lighter buyers tend to forward telescope their buying episodes. Heavier buyers tend either to over-use a rate-based estimation of once-a-month buying and over-report purchases at multiples of six or to use round numbers. These errors lead to overestimates of penetration and average purchase frequency. Adjusting the aggregate data for the NBD, however, improves the accuracy of these metrics.

In light of the importance of purchase data for decision making, the authors describe the inaccuracy problem in frequency reports and offer practical suggestions regarding the correction of survey data.

Two novel contributions are offered here: an investigation of errors in different buyer groups and use of the NBD in survey accuracy research.

With the recognition that generating economic growth is not the same as sustaining it, the challenge to catch-up and growth literature is discerning between these processes. Recent research suggests that the decline in the frequency of “shrinking” episodes is more important for long-term development than higher growth rates. By using a framework centred around social capabilities, this study aims to investigate the effects of income inequality and poverty on economic shrinking frequency, as opposed to previous literature that has exclusively had a growth focus. The aim is to investigate how and why some societies might be more resilient to economic shrinking.

The research is a quantitative study, and the authors build a longitudinal data set including 23 developing countries throughout 42 years to test the paper’s purpose. This study uses country and period fixed-effects specifications as well as cross-sectional graphical representations to investigate the relationship between proxies of economic inclusivity and the frequency of shrinking episodes.

The authors demonstrate that while inclusive societies are more resilient to shrinking overall, it is changes in poverty levels, but not changes in income inequality, that appear to be correlated with economic shrinking frequency. Inequality, while still an important element to explain countries’ growth potential as an initial condition, does not seem to make the sample more resilient to shrinking. The authors conclude that the mechanisms in which poverty and inequality are correlated with the catch-up process must run through different channels. Ultimately, processes that explain growth may intersect but not always overlap with the ones that explain resilience to shrinking.

The need for inclusive growth in long-term development has been championed for decades, yet inclusion has seldom been explored from the shrinking perspective. Though poverty reduction is already an important mainstream political objective, this paper differentiates itself by providing an alternate viewpoint of why this is important. Income inequality could have more of an economic growth limiting effect, while poverty reduction could be required to build resilience to economic shrinking. Developing countries will need both growth and resilience to shrinking, to catch-up with higher-income economies, which policymakers might need to balance carefully.

The use of frequency output for measurement transmission remains common in the design of smart transmitters. Conventional methods of frequency generation, based on counting clock cycles, have a precision which is inversely proportional to the frequency to be generated. Consequently, frequency output precision could be much lower than the measurement precision. This paper describes a simple frequency generation technique which, when implemented in low‐cost hardware, provides a precision of 10⁻⁶ per cent for all frequencies. The method represents an intermediate non‐available frequency by dithering between two exact frequencies. Averaging over some reasonably short timescale provides the desired frequency to high precision.

Customer differences and similarities play a crucial role in service operations, and service industries need to develop various strategies for different customer types. This study aims to understand the behavioral pattern of customers in the banking industry by proposing a hybrid data mining approach with rule extraction and service operation benchmarking.

The authors analyze customer data to identify the best customers using a modified recency, frequency and monetary (RFM) model and K-means clustering. The number of clusters is determined with a two-step K-means quality analysis based on the Silhouette, Davies–Bouldin and Calinski–Harabasz indices and the evaluation based on distance from average solution (EDAS). The best–worst method (BWM) and the total area based on orthogonal vectors (TAOV) are used next to sort the clusters. Finally, the associative rules and the Apriori algorithm are used to derive the customers' behavior patterns.

As a result of implementing the proposed approach in the financial service industry, customers were segmented and ranked into six clusters by analyzing 20,000 records. Furthermore, frequent customer financial behavior patterns were recognized based on demographic characteristics and financial transactions of customers. Thus, customer types were classified as highly loyal, loyal, high-interacting, low-interacting and missing customers. Eventually, appropriate strategies for interacting with each customer type were proposed.

The authors propose a novel hybrid multi-attribute data mining approach for rule extraction and the service operations benchmarking approach by combining data mining tools with a multilayer decision-making approach. The proposed hybrid approach has been implemented in a large-scale problem in the financial services industry.