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This teaching case aims to encourage reflection on the formulation of the strategic repositioning of an automotive dealership.

Built on the teaching case method, it portrays the situation of the Premium Motors automotive dealership in the face of a strategic repositioning. Management and the board discuss ways to minimize the impact of the coronavirus disease 2019 (COVID-19) pandemic on sales figures. The situation presents the possibility of entering a new product segment.

How to make consumers correctly understand the new positioning? The case is structured to enable reflection and teaching of marketing strategies, with each student having the possibility of putting themselves in the role of company managers. The environment and trajectory are also portrayed, broadening the perception of the studied company and providing devices for solving the emerging problems of the case.

The case becomes a tool to promote knowledge, from the implementation to the management of strategic repositioning. Thus, the teaching notes offer directions on how professors can use the teaching case with their undergraduate and graduate students in disciplines related to strategy and marketing.

Artificial intelligence (AI) offers many benefits to improve predictive marketing practice. It raises ethical concerns regarding customer prioritization, market share concentration and consumer manipulation. This paper explores these ethical concerns from a contemporary perspective, drawing on the experiences and perspectives of AI and predictive marketing professionals. This study aims to contribute to the field by providing a modern perspective on the ethical concerns of AI usage in predictive marketing, drawing on the experiences and perspectives of professionals in the area.

The study conducted semistructured interviews for 6 weeks with 14 participants experienced in AI-enabled systems for marketing, using purposive and snowball sampling techniques. Thematic analysis was used to explore themes emerging from the data.

Results reveal that using AI in marketing could lead to unintended consequences, such as perpetuating existing biases, violating customer privacy, limiting competition and manipulating consumer behavior.

The authors identify seven unique themes and benchmark them with Ashok’s model to provide a structured lens for interpreting the results. The framework presented by this research is unique and can be used to support ethical research spanning social, technological and economic aspects within the predictive marketing domain.

La Inteligencia Artificial (IA) ofrece muchos beneficios para mejorar la práctica del marketing predictivo. Sin embargo, plantea preocupaciones éticas relacionadas con la priorización de clientes, la concentración de cuota de mercado y la manipulación del consumidor. Este artículo explora estas preocupaciones éticas desde una perspectiva contemporánea, basándose en las experiencias y perspectivas de profesionales en IA y marketing predictivo. El estudio tiene como objetivo contribuir a la literatura de este ámbito al proporcionar una perspectiva moderna sobre las preocupaciones éticas del uso de la IA en el marketing predictivo, basándose en las experiencias y perspectivas de profesionales en el área.

Para realizar el estudio se realizaron entrevistas semiestructuradas durante seis semanas con 14 participantes con experiencia en sistemas habilitados para IA en marketing, utilizando técnicas de muestreo intencional y de bola de nieve. Se utilizó un análisis temático para explorar los temas que surgieron de los datos.

Los resultados revelan que el uso de la IA en marketing podría tener consecuencias no deseadas, como perpetuar sesgos existentes, violar la privacidad del cliente, limitar la competencia y manipular el comportamiento del consumidor.

El estudio identifica siete temas y los comparan con el modelo de Ashok para proporcionar una perspectiva estructurada para interpretar los resultados. El marco presentado por esta investigación es único y puede utilizarse para respaldar investigaciones éticas que abarquen aspectos sociales, tecnológicos y económicos dentro del ámbito del marketing predictivo.

人工智能（AI）为改进预测营销实践带来了诸多益处。然而, 这也引发了与客户优先级、市场份额集中和消费者操纵等伦理问题相关的观点。本文从当代角度深入探讨了这些伦理观点, 充分借鉴了人工智能和预测营销领域专业人士的经验和观点。旨在通过现代视角提供关于在预测营销中应用人工智能时所涉及的伦理观点, 为该领域做出有益贡献。

本研究采用了目的性和雪球抽样技术, 与14位在人工智能营销系统领域具有丰富经验的参与者进行为期六周的半结构化访谈。研究采用主题分析方法, 旨在深入挖掘数据中显现的主要主题。

研究结果表明, 在营销领域使用人工智能可能引发一系列意外后果, 包括但不限于加强现有偏见、侵犯客户隐私、限制竞争以及操纵消费者行为。

本研究通过明确定义七个独特的主题, 并采用阿肖克模型进行基准比较, 为读者提供了一个结构化的视角, 以解释研究结果。所提出的框架具有独特之处, 可有效支持在跨足社会、技术和经济领域的预测营销中展开的伦理研究。

The purpose of this paper is to investigate how to optimise corporate social responsibility (CSR) when communicating via social media. In particular, the communication type, cause proximity and CSR motives are addressed facing the increased demand for transparency and the grown consumers' expectations towards socially responsible brands.

Online survey was developed, based on a profound literature review and a field research we did on the actual social media behaviour of socially responsible brands. Consumers attitudes and behavioural reactions in terms of willingness to participate in a campaign, to spread e-WOM and to purchase were investigated, as a function of CSR motives (value vs performance vs value and performance) and cause proximity (national vs international), respectively, with monologue (study 1) and dialogue communication type (study 2).

Cause proximity enhanced the campaign participation, and this effect was pronounced for both, monologue and dialogue type of communication. CSR motives modulated the willingness to spread electronic word-of-mouth, and this holds for both, monologue and dialogue communication. Attitudes and purchase intention were highest when value- and performance-driven motives were communicated, but these effects appeared only when the message was in a dialogue form of communication. Message credibility and CSR motives credibility perception further modulated consumers response.

The outcomes could be used in developing marketing (communication) strategies leading to values and revenues optimisation.

The results are discussed in a framework of how CSR resonates via social media.