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Purpose – The advancement of multimedia technology has spurred the use of multimedia in business practice. The adoption of audio and visual data will accelerate as marketing scholars become more aware of the value of audio and visual data and the technologies required to reveal insights into marketing problems. This chapter aims to introduce marketing scholars into this field of research.Design/methodology/approach – This chapter reviews the current technology in audio and visual data analysis and discusses rewarding research opportunities in marketing using these data.Findings – Compared with traditional data like survey and scanner data, audio and visual data provides richer information and is easier to collect. Given these superiority, data availability, feasibility of storage, and increasing computational power, we believe that these data will contribute to better marketing practices with the help of marketing scholars in the near future.Practical implications: The adoption of audio and visual data in marketing practices will help practitioners to get better insights into marketing problems and thus make better decisions.Value/originality – This chapter makes first attempt in the marketing literature to review the current technology in audio and visual data analysis and proposes promising applications of such technology. We hope it will inspire scholars to utilize audio and visual data in marketing research.

The malfunction variables of power stations are related to the areas of weather, physical structure, control and load behaviour. To predict temporal power failure is difficult due to their unpredictable characteristics. As high accuracy is normally required, the estimation of failures of short-term temporal prediction is highly difficult. This study presents a method for converting stochastic behaviour into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms are used to perform the Short-term estimation. The environment, the operation and the generated signal factors are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a data set. Monte-Carlo simulation using MATLAB programming has been used to conduct experimental estimation of failures. The estimated failures of the experiment are then compared with the actual system temporal failures and found to be in good match. Therefore, for any future power grid, there is a testbed ready to estimate the future failures.

Massive shifts in the recruitment landscape, the continually changing nature of work and workers, and extraordinary technological progress have combined to enable unparalleled advances in how current and prospective employees receive and process information about organizations. Once the domain of internal organizational public relations and human resources (HR) teams, most employment branding has moved beyond organizations’ control. This chapter provides a conceptual framework pertaining to third party employment branding, defined as communications, claims, or status-based classifications generated by parties outside of direct company control that shape, enhance, and differentiate organizations’ images as favorable or unfavorable employers. Specifically, the authors first theorize about the underlying mechanisms by which third party employment branding might signal prospective and current employees. Second, the authors develop a framework whereby we comprehensively review third party employment branding sources, thus identifying the different ways that third party employment branding might manifest. Third, using prototypical examples, the authors link the various signaling mechanisms to the various third party employment branding sources identified. Finally, the authors propose an ambitious future research agenda that considers not only the positive aspects of third party employment branding but also potential “dark sides.” Thus, the authors view this chapter as contributing to the broader employment branding literature, which should enhance scholarly endeavors to study it and practitioner efforts to leverage it.