Résultats de recherche pour « Digital marketing »

Generative Artificial Intelligence (GAI) is increasingly being integrated into marketing education and is reshaping the skillsets required in marketing careers. While research has highlighted the promise and perils of incorporating GAI into education, there remains a need for a comprehensive framework to guide its effective use. In this research, we conduct a multipronged analysis, including a review of marketing course syllabi, a survey of marketing educators, and follow-up qualitative interviews. Building on Role Theory and the Community of Inquiry (CoI) model, we propose that GAI can assume three roles in marketing education: tutor, teammate, and tool. Each role influences teaching, social, and cognitive presence differently, shaping the learning experience and preparing workplace-ready marketing graduates. For instance, as a tutor, GAI can aid students in grasping theoretical concepts, while as a teammate, it can foster collaboration by supporting brainstorming and problem-solving activities. However, ethical considerations such as data privacy, plagiarism, dependency on AI, and fairness in assessment must be addressed to ensure its responsible adoption in marketing education. We provide concrete examples for GAI's careful integration in marketing courses, and its implications for marketing educators, learners, and policymakers.

Digital marketing is the marketing of products or services using digital technologies, mainly on the Internet, but also including mobile phones, display advertising, and any other digital medium.Digital marketing's development since the 1990s and 2000s has changed the way brands and businesses use technology for marketing.As digital platforms are increasingly incorporated into marketing plans and everyday life, and as people use digital devices instead of visiting physical shops, digital marketing campaigns are becoming more prevalent and efficient.This paper mainly focuses on conceptual understanding of digital marketing, how digital marketing helps today's business and some cases in the form of examples.

Here a collection of 1169 abstracts, which corresponds to articles that the Journal of Marketing Research has published from 2005 to 2014, are analysed under a novel approach. We apply several statistical methods, such as Principal Components Analysis and Correspondence Analysis to identify the way Marketing vocabulary is evolving. Similarly those articles that introduce new vocabulary are identified and the preferred words by authors are also detected. In order to provide an easy-to-understand explanation, we provide our results graphically. A word-cloud with the most frequent words is given first. Secondly abstracts-words are represented on the factorial plane. Finally one representation of word-years allows us to detect changes on the vocabulary through the passing of time.

Purpose – The purpose of this paper is to provide insights into the utilization and goals of digital marketing, and examines factors that influence the adoption and use of digital marketing channels in SMEs. Design/methodology/approach – The data comprises semi-structured theme interviews in SMEs among 16 managers and 421 survey respondents in Central Finland. Findings – The results of this study reveal that SMEs seem not use the full potential of the new digital tools, and so are not deriving benefit from the opportunities they provide. Furthermore, the results also raise the question of whether SMEs have understood the fundamental change in the nature of communication brought about by digitization. Research limitations/implications – The data comes from one region and thus the research context limits the generalizability of the results. Practical implications – SMEs seem not to be keeping pace with digital developments, mostly due to the lack of knowledge of digital marketing. Most of the studied SMEs do not apply the full potential of the new digital tools and hence are not benefitting fully from them. Social implications – Discussions on the future regional development of SMEs have called for training programmes to help SMEs exploit digitization. This is something that the government should take note of. Originality/value – Whereas the adoption process of new technologies such as IT in general and the internet in particular have been examined in the SME literature, this is among the first studies examining adoption and usage of digital tools from the marketing perspective.

Fashion styles adopted every day are an important aspect of culture, and style trend analysis helps provide a deeper understanding of our societies and cultures. To analyze everyday fashion trends from the humanities perspective, we need a digital archive that includes images of what people wore in their daily lives over an extended period. In fashion research, building digital fashion image archives has attracted significant attention. However, the existing archives are not suitable for retrieving everyday fashion trends. In addition, to interpret how the trends emerge, we need non-fashion data sources relevant to why and how people choose fashion. In this study, we created a new fashion image archive called Chronicle Archive of Tokyo Street Fashion (CAT STREET) based on a review of the limitations in the existing digital fashion archives. CAT STREET includes images showing the clothing people wore in their daily lives during the period 1970--2017, which contain timestamps and street location annotations. We applied machine learning to CAT STREET and found two types of fashion trend patterns. Then, we demonstrated how magazine archives help us interpret how trend patterns emerge. These empirical analyses show our approach's potential to discover new perspectives to promote an understanding of our societies and cultures through fashion embedded in consumers' daily lives.

Digitalization is making a significant impact on marketing. New marketing approaches and tools are emerging which are not always clearly categorised. This article seeks to investigate the relationship between one of the novel marketing tools, content marketing, and the five elements of the traditional marketing communication mix. Based on an extensive literature review, this paper analyses the main differences and similarities between them. This article aims to generate a debate on the status of content marketing. According to the authors' opinion, content marketing can be considered as the sixth marketing communication mix element. However, further research is needed to fill in the existing knowledge gap.

The digital marketing discipline is facing growing fragmentation; the proliferation of different subareas of research impedes the accumulation of knowledge. This fragmentation seems logically tied to the inherent complexity of the Internet, itself resulting from 50 years of evolution. Thus, our aim is to provide an integrative framework for research in digital marketing derived from the historical analysis of the Internet. Using practice theory and institutional theory, we outline a new type of institutional work: imprinting work. We apply this framework to the analysis of historical secondary sources. We find four cultural repertoires on the Internet (collaborative systems, traditional market systems, co-creation systems, and prosumption market systems) and describe the dynamics of imprinting work leading to their creation, showing how new systems are created by appropriating and assimilating existing cultural repertoires. We contribute to the digital marketing literature by providing a cultural framework and a theory explaining the dynamics of the creation of four cultural repertoires. Moreover, we outline three paths of potential evolution of the digital landscape. Our framework may help managers make sense of their digital strategy and navigate the various Internet systems.

This paper explores using a deep learning Long Short-Term Memory (LSTM) model for accurate stock price prediction and its implications for portfolio design. Despite the efficient market hypothesis suggesting that predicting stock prices is impossible, recent research has shown the potential of advanced algorithms and predictive models. The study builds upon existing literature on stock price prediction methods, emphasizing the shift toward machine learning and deep learning approaches. Using historical stock prices of 180 stocks across 18 sectors listed on the NSE, India, the LSTM model predicts future prices. These predictions guide buy/sell decisions for each stock and analyze sector profitability. The study's main contributions are threefold: introducing an optimized LSTM model for robust portfolio design, utilizing LSTM predictions for buy/sell transactions, and insights into sector profitability and volatility. Results demonstrate the efficacy of the LSTM model in accurately predicting stock prices and informing investment decisions. By comparing sector profitability and prediction accuracy, the work provides valuable insights into the dynamics of the current financial markets in India.