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Shopper marketing describes the planning and execution of all marketing activities that influence a shopper along – and beyond – the path-to-purchase, from shopping trigger to purchase, consumption, repurchase, and recommendation stages. Shopper marketing practices at manufacturers and retailers are growing at a tremendous pace and a rising portion of marketing budgets are now devoted to shopper marketing. The first phase of shopper marketing research and practice, Shopper Marketing 1.0, addressed interesting issues, primarily relating to in-store marketing. In the next phase, Shopper Marketing 2.0, will significantly extend to out-of-store marketing, including online and mobile marketing, resulting in an integrated practice. In this new environment, to formulate and execute effective shopper marketing strategies, managers need to better understand the complete picture of how online, offline, mobile and in-store marketing influence shoppers in the path-to-purchase-and-beyond cycle. In this chapter, we present the evolution of shopper marketing, summarize key learnings, outline important issues, and discuss the opportunities and challenges of Shopper Marketing 2.0.

We investigate how online price dispersion has evolved since the bursting of the Internet bubble by comparing price dispersion levels in years 2000, 2001, and 2003 and between multi-channel and pure play e-tailers. The results show that although online price dispersion declined between 2000 and 2001 when there was a shakeout in Internet retailing, it increased from 2001 to 2003, the post bubble period, in particular, for desktop computers, laptop computers, PDAs, electronics and software. The proportion of items for which price dispersion at multi-channel retailers was higher than that at pure play e-tailers, increased steadily during 2000–2003. These findings suggest that online price dispersion is persistent even as Internet markets mature.

Consumer behavior has evolved because of technological development. Nowadays, consumers carry out the different stages of the decision-making process by combining multiple devices which has been defined as multi, cross and omnichannel behavior. These behaviors have attracted the attention of academics and become a hot topic in literature. As a result, vast amounts of studies on the subject need to be revised and clarified. Thus, the aim of this chapter is to synthetize the primary academic literature that analyzes multi, cross and omnichannel behavior from the consumer point of view. To do that, first, the main concepts (multi, cross and omnichannel) and their differences are clarified. Second, the major findings of channel mix literature regarding the topics, channel scope and theories are exposed and described. Third, the opportunities and future lines of research are presented. This chapter contributes to the literature by clarifying the conceptualization of multi, cross and omnichannel behaviors; offering a complete picture of the main topics, channel approaches and theories addressed in channel mix literature; and presenting future research opportunities and open research questions in a channel mix context that could serve as a starting point to build further research.

With the rapid advancement of lifestyle and technology, human lives are becoming increasingly threatened. Accidents, exposure to dangerous substances and animal strikes are all possible threats. Human lives are increasingly being harmed as a result of attacks by wild animals. Further investigation into the cases reported revealed that such events can be detected early on. Techniques such as machine learning and deep learning will be used to solve this challenge. The upgraded VGG-16 model with deep learning-based detection is appropriate for such real-time applications because it overcomes the low accuracy and poor real-time performance of traditional detection methods and detects medium- and long-distance objects more accurately. Many organizations use various safety and security measures, particularly CCTV/video surveillance systems, to address physical security concerns. CCTV/video monitoring systems are quite good at visually detecting a range of attacks associated with suspicious behavior on the premises and in the workplace. Many have indeed begun to use automated systems such as video analytics solutions such as motion detection, object/perimeter detection, face recognition and artificial intelligence/machine learning, among others. Anomaly identification can be performed with the data collected from the CCTV cameras. The camera surveillance can generate enormous quantities of data, which is laborious and expensive to screen for the species of interest. Many cases have been recorded where wild animals enter public places, causing havoc and damaging lives and property. There are many cases where people have lost their lives to wild attacks. The conventional approach of sifting through images by eye can be expensive and risky. Therefore, an automated wild animal detection system is required to avoid these circumstances.

The proposed system consists of a wild animal detection module, a classifier and an alarm module, for which video frames are fed as input and the output is prediction results. Frames extracted from videos are pre-processed and then delivered to the neural network classifier as filtered frames. The classifier module categorizes the identified animal into one of the several categories. An email or WhatsApp notice is issued to the appropriate authorities or users based on the classifier outcome.

Evaluation metrics are used to assess the quality of a statistical or machine learning model. Any system will include a review of machine learning models or algorithms. A number of evaluation measures can be performed to put a model to the test. Among them are classification accuracy, logarithmic loss, confusion matrix and other metrics. The model must be evaluated using a range of evaluation metrics. This is because a model may perform well when one measurement from one evaluation metric is used but perform poorly when another measurement from another evaluation metric is used. We must utilize evaluation metrics to guarantee that the model is running correctly and optimally.

The output of conv5 3 will be of size 7*7*512 in the ImageNet VGG-16 in Figure 4, which operates on images of size 224*224*3. Therefore, the parameters of fc6 with a flattened input size of 7*7*512 and an output size of 4,096 are 4,096, 7*7*512. With reshaped parameters of dimensions 4,096*7*7*512, the comparable convolutional layer conv6 has a 7*7 kernel size and 4,096 output channels. The parameters of fc7 with an input size of 4,096 (i.e. the output size of fc6) and an output size of 4,096 are 4,096, 4,096. The input can be thought of as a one-of-a-kind image with 4,096 input channels. With reshaped parameters of dimensions 4,096*1*1*4,096, the comparable convolutional layer conv7 has a 1*1 kernel size and 4,096 output channels. It is clear that conv6 has 4,096 filters, each with dimensions 7*7*512, and conv7 has 4,096 filters, each with dimensions 1*1*4,096. These filters are numerous, large and computationally expensive. To remedy this, the authors opt to reduce both their number and the size of each filter by subsampling parameters from the converted convolutional layers. Conv6 will use 1,024 filters, each with dimensions 3*3*512. Therefore, the parameters are subsampled from 4,096*7*7*512 to 1,024*3*3*512. Conv7 will use 1,024 filters, each with dimensions 1*1*1,024. Therefore, the parameters are subsampled from 4,096*1*1*4,096 to 1,024*1*1*1,024.

To address the safety concerns generated by truck crashes occurred in big cities, this paper analyzes the zip code tabulation area (ZCTA)-based truck crash frequency across four temporal intervals – morning (6:00–10:00), mid-day (10:00–15:00), afternoon (15:00–19:00), and night (19:00–6:00) in New York City in 2010. A multivariate conditional autoregressive count model is used to recognize both spatial and temporal dependences. The results prove the presence of spatial and temporal dependencies for truck crashes that occurred in neighboring areas. Built environment attributes such as various types of business establishment density and traffic volume for different types of vehicles, which are important factors to consider for crashes occurred in an urban setting, are also examined in the study.

In this paper, we first develop a game theoretic model of price competition between a pure play e-tailer and a bricks-and-clicks e-tailer. We show that in general, the pure play e-tailer has a lower equilibrium price. We then develop a simultaneous equation model of e-tailer price and traffic and estimate this model using data collected from 905 e-tailers across eight product categories. The empirical results show that after controlling for the effects of other variables, prices at pure play e-tailers are generally lower. E-tailers with high traffic do not always command higher prices. E-tailers with high level of reliability, shopping convenience, and deep information, generally do not generate high web traffic and do not enjoy high prices. However, trust enhances e-tailer traffic and early online entry is associated with both high e-tailer traffic and high prices.

Purpose: This chapter is based on risk management of the insurance sector with reinsurance as its linchpin. Such is the importance of the insurance sector that its risk management must be considered.

Need for the study: Risk management of various sectors is gaining much attention. The insurance sector, known to manage the risk of multiple sectors, also requires its own chance to be controlled with the same or even more intensity. Considering the importance of reinsurance coupled with the dependency of primary insurers on reinsurers and the absence of research on reinsurers, the need to conduct a comprehensive study on the topic is felt.

Methodology: It will be a conceptual chapter based on the rigorous literature on the topic integrated with the researcher’s insights to bring forth the framework of reinsurers for the readers.

Findings: It is found that insurers can themselves become the victims of the financial crisis in case they insure risks that surpass their economic boundaries. Not only this, the failure of insurance companies can have a ripple effect on the country’s economy. Therefore, insurers must possess financial resilience; to remain so, they need to have prudent management of the risk they are undertaking.

Practical implications: The study covers a relatively less researched area of reinsurance and hence has a vast scope of research in the future. The study would be helpful to stakeholders like regulators and primary insurers. It will unveil the paradigm of reinsurance and enlighten the stakeholders on how to use it effectively.

This article discusses how the COVID-19 pandemic affects the demand and preferences of tourists in Russia. Tourists are the important part of the tourism industry, the most affected by COVID-19, because they are not only limited in moving and free choice of vacation destinations but also experience the economic consequences of the pandemic. However, the industry has already adapted to the current realities of the pandemic period, and tourists largely accept closed borders, mandatory vaccination requirements and PCR tests as an integral part of a modern tourist trip.

In Russia, the tourism industry has received a significant development boost despite the expected crisis consequences. The efforts of the authorities to stimulate domestic tourism and the lack of alternatives for vacations contributed to the fact that in 2020–2021 the domestic tourist flow increased by several times.

Article is aimed at studying the changes in tourist motivation and preferences of Russian tourists made by ongoing COVID-19 pandemic. However, will Russians still want to travel across their country after the pandemic is over? And what is currently influencing their choice of vacation forms and destinations? The survey revealed changes in the tourists' choice of vacation destinations, preferences in forms of activity and duration of travel, decrease of interest in package tours and rise of individual trips as well as the stable degree of satisfaction with vacations in Russia.

To answer this question, we examined the results of public opinion survey, carried out by All-Russian Centre for the Public Opinion Study and Association of Russian Tour operators. To confirm these results, we conducted own tourist opinion survey and analysed reviews on popular travel platforms in Russia. Today the tourism industry is in uncertainty and it is impossible to predict its development and tourist motivation in long-term perspective. This makes the study promising for continuation in the long term.

Recent cybersecurity education literature has focused on developments in cybersecurity curricula, qualifications and accreditation, pedagogy and practice to increase the number of cybersecurity professionals, in both the UK and internationally. There has been little research published to date on the online learning, teaching and assessment environment as a cyber target in its own right. This chapter appraised and discussed the dangers in, and emerging threats to, using online environments. It proposes a set of steps and mitigation measures that can be taken to make it more difficult for cybercriminals to attack educational institutions.